Method and apparatus for adaptive line enhancement in Coriolis mass flow meter measurementFlow Meter Abstract: Flow Meter Claims: 1. An apparatus for measuring mass flow rate of a material in a Coriolis mass flow meter having a flow tube and having first and second sensors associated with said flow tube for generating output signals indicative of the oscillatory movement of said flow tube, said apparatus comprising: analog to digital converter means for periodically sampling said sensor output signals and for converting sampled sensor output signals to digital form to generate a sequence of discrete sampled values representative of said output signals, including any undesirable components, of each of said first and second sensors; digital notch filtration means, responsive to the generation of said sequence of discrete sampled values, for generating a sequence of discrete enhanced values, each discrete enhanced value corresponding to a sample of said sequence of discrete sampled values with said undesirable components removed; phase value determination means, responsive to the generation of said sequence of discrete enhanced values, for generating the phase values of the oscillatory movement of said flow tube indicated by said sequence of discrete enhanced values; phase difference means, responsive to the generation of said phase values, for determining a phase difference between the output signals of said first and second sensors; and mass flow measurement means, responsive to the determination of phase difference, for determining a mass flow rate value of the material flowing through the flow tube. 2. The apparatus of claim 1 further comprising: notch adaptation means, cooperative with said digital notch filtration means, for altering filter parameters of said digital notch filtration means to affect the notch capability to reject undesirable components of the output signals of said first and second sensors. 3. The apparatus of claim 2 wherein said filter parameters comprise variable polynomial coefficients applied to said discrete sampled values to enhance said discrete sampled values, wherein said variable polynomial coefficients determine the center frequency of the notch of said digital notch filtration means. 4. The apparatus of claim 3 wherein said notch adaptation means further comprises weight adaptation means for adjusting said variable polynomial coefficients applied to said discrete sampled values in enhancing said discrete sampled values to alter the center frequency of said digital notch filtration means. 5. The apparatus of claim 4 further comprising: means for detecting that the ratio of a discrete enhanced value to a corresponding noise signal has fallen below a predetermined threshold value and for generating a fault signal responsive to said detection; and means responsive to generation of said fault signal for adjusting said variable polynomial coefficients. 6. The apparatus of claim 4 further comprising: stability test means for detecting instability in the adjustment of said variable polynomial coefficients and for generating an instability signal to indicate detection of the instability; and means responsive to generation of said instability signal to adjust said variables polynomial coefficients to reduce said instability. 7. The apparatus of claim 2 wherein said filter parameters comprise a variable debiasing parameter applied to said discrete sampled values in enhancing said discrete sampled values, wherein said variable debiasing parameter determines the frequency spectrum width of the notch of said digital notch filtration means. 8. The apparatus of claim 7 wherein said notch adaptation means further comprises weight adaptation means for adjusting said variable debiasing parameter applied to said discrete sampled values in enhancing said discrete sampled values. 9. The apparatus of claim 8 further comprising: means for detecting that the ratio of a discrete enhanced value to a corresponding noise signal has fallen below a predetermined threshold value and for generating a fault signal responsive to the detection; and means responsive to said fault signal for adjusting said variable debiasing parameter. 10. The apparatus of claim 1 wherein said phase difference means further comprises: windowing means for defining a window comprising a plurality of sequential ones of said discrete enhanced values; and Goertzel filtration means for decimating the discrete enhanced values in said window to determine a phase value for said window. 11. The apparatus of claim 10 further comprising Hanning windowing means for weighting each of said plurality of discrete enhanced values in said window wherein the weights are determined as: h(k)=(1/2) (1-cos (2.pi.k/(2N-1))) where: N is half the number of discrete enhanced values in the window, and k is the index of the value to which the weight h(k) is applied. 12. The apparatus of claim I wherein said phase difference means further comprises: windowing means for defining a plurality of windows, each of said plurality of windows comprising a plurality of sequential ones of said discrete enhanced values; and Goertzel filtration means for decimating said plurality of discrete enhanced values in each of said plurality of windows to determine a phase value for said each of said plurality of windows, wherein each of said windows comprises an equal number of said discrete enhanced values and wherein each of said windows is offset from an earlier window by an equal number of said discrete enhanced values. 13. The apparatus of claim 12 further comprising Hanning windowing means for weighting each of said plurality of discrete enhanced values in each of said windows wherein the weights are determined as: h(k)=(1/2) (1-cos (2.pi.k/(2N-1))) where: N is half the number of discrete enhanced values in the window, and k is the index of the value to which the weight h(k) is applied. 14. The apparatus of claim 1 wherein said digital notch filtration means further comprises: first digital notch filtration means, responsive to the generation of said sequence of discrete sampled values, for generating an intermediate sequence of discrete values, each intermediate discrete value corresponding to a sample of said sequence of discrete sampled values with signals representative of said undesirable components partially removed; and second digital notch filtration means, responsive to the generation of said intermediate sequence of discrete values, for generating said sequence of discrete enhanced values, each discrete enhanced value corresponding to an intermediate discrete value of said intermediate sequence of discrete values with signals representative of said undesirable components removed. 15. The apparatus of claim 14 further comprising: first notch adaptation means, cooperative with said first digital notch filtration means, for altering filter parameters of said first digital notch filtration means which are determinative of the characterization of signals as representative of noise; and second notch adaptation means, cooperative with said second digital notch filtration means, for altering filter parameters of said second digital notch filtration means which are determinative of the characterization of signals as representative of noise. 16. The apparatus of claim 15 wherein said filter parameters comprise variable polynomial coefficients applied to said discrete sampled values to enhance said discrete sampled values. 17. The apparatus of claim 16 wherein said first notch adaptation means further comprises weight adaptation means for adjusting said variable polynomial coefficients applied to said discrete sampled values to generate said intermediate sequence of discrete values, and wherein said second notch adaptation means further comprises weight adaptation means for adjusting variable polynomial coefficients applied to said intermediate sequence of discrete values to generate said sequence of discrete enhanced values. 18. In a Coriolis mass flow meter having a flow tube and having first and second sensors associated with the flow tube for generating output signals indicative of the oscillatory movement of the flow tube, a method for measuring the mass flow rate of a material flowing through said flow tube of said flow meter comprising the steps of: periodically converting analog output signals generated by the first and second sensors into digital form to generate a sequence of discrete sampled values representative of said output signals, including any undesirable components, of each of said first and second sensors; applying said sequence of discrete sampled values to digital notch filtration means to generate a sequence of discrete enhanced values, each discrete enhanced value corresponding to a sample of said sequence of discrete sampled values with signals representative of noise removed; applying said sequence of discrete enhanced values to phase value determination means to determine phase values of the oscillatory movement of the flow tube indicated by said sequence of said discrete enhanced values; applying said phase values to phase difference computation means to determine a phase difference between the output signals of said first and second sensors; and determining the mass flow rate of the material flowing through said flow meter responsive to the determination of phase difference. 19. The method of claim 18 further comprising: altering filter parameters of said digital notch filtration means to adjust the digital notch filtration means to compensate for changes in the frequency of oscillations of the flow tube. 20. The method of claim 19 wherein said filter parameters comprise variable polynomial coefficients applied to said discrete sampled values to enhance said discrete sampled values, wherein said variable polynomial coefficients determine the center frequency of the notch of said digital notch filtration means. 21. The method of claim 20 wherein the altering step further comprises adjusting said variable polynomial coefficients applied to said discrete sampled values in enhancing said discrete sampled values to alter the center frequency of said digital notch filtration means. 22. The method of claim 21 further comprising: determining a ratio of a discrete enhanced value to a corresponding noise signal; determining whether said ratio has fallen below a predetermined threshold value; generating a fault signal responsive to the determination that said ratio has fallen below said predetermined threshold value; and adjusting said variable polynomial coefficients responsive to generation of said fault signal. 23. The method of claim 21 further comprising: determining whether said variable polynomial coefficients are outside an acceptable range of stable values; generating an instability signal responsive to a determination that said variable polynomial coefficients are unstable; and adjusting said variable polynomial coefficients responsive to generation of said instability signal to reduce said instability. 24. The method of claim 19 wherein said filter parameters comprise a variable debiasing parameter applied to said discrete sampled values in enhancing said discrete sampled values, wherein said variable debiasing parameter determines the frequency spectrum width of the notch of said digital notch filtration means. 25. The method of claim 24 wherein said step of altering said parameters further comprises adjusting said variable debiasing parameter applied to said discrete sampled values for enhancing said discrete sampled values. 26. The method of claim 25 further comprising: determining a ratio of a discrete enhanced value to a corresponding noise signal; determining whether said ratio has fallen below a predetermined threshold value; generating a fault signal responsive to the determination that said ratio has fallen below said predetermined threshold value; and adjusting said variable debiasing parameter responsive to generation of said fault signal. 27. The method of claim 18 wherein the application of said phase values to said phase difference computation means further comprises: defining a window comprising a plurality of sequential ones of said discrete enhanced values; and decimating the discrete enhanced values in said window through a Goertzel filtration to determine a phase value for said window. 28. The method of claim 27 further comprising: determining a Hanning window weight for weighting each of said plurality of discrete enhanced values in said window wherein the weights are determined as: h(k)=(1/2)(1-cos (2.pi.k/(2N-1))) where: N is half the number of discrete enhanced values in the window, and k is the index of the value to which the weight h(k) is applied. 29. The method of claim 18 wherein the application of said phase values to said phase difference computation means further comprises: defining a plurality of windows, each of said plurality of windows comprising a plurality of sequential ones of said discrete enhanced values; and decimating the discrete enhanced values in each of said plurality of windows through a Goertzel filter to determine a phase value for said each of said plurality of windows, wherein each of said windows comprises an equal number of said discrete enhanced values and wherein each of said windows is offset from an earlier window by an equal number of said discrete enhanced values. 30. The method of claim 29 further comprising: determining a Hanning window weight for weighting each of said plurality of discrete enhanced values in each of said plurality of windows wherein the weights are determined as: h(k)=(1/2) (1-cos (2.pi.k/(2N-1))) where: N is half the number of discrete enhanced values in the window, and k is the index of the value to which the weight h(k) is applied. 31. The method of claim 18 wherein the step of applying said discrete sampled values to said digital notch filtration means further comprises: operating a first digital notch filtration means to generate an intermediate sequence of discrete values, each intermediate discrete value corresponding to a sample of said sequence of discrete sampled values with signals representative of noise partially removed; and operating a second digital notch filtration means responsive to the generation of said intermediate sequence of discrete values to generate a sequence of discrete enhanced values, each discrete enhanced value corresponding to an intermediate discrete value of said intermediate sequence of discrete values with signals representative of noise removed. 32. The method of claim 31 further comprising: operating first notch adaption means to alter filter parameters of the first digital notch filtration means to adjust the first digital notch filtration means to compensate for changes in the frequency of oscillations of the flow tube; and operating second notch adaption means to alter filter parameters of the second digital notch filtration means to adjust the second digital notch filtration means to compensate for changes in the frequency of oscillations of the flow tube. 33. The method of claim 32 wherein said filter parameters comprise variable polynomial coefficients applied to said discrete sampled values to enhance said discrete sampled values. 34. The method of claim 33 wherein said step of altering said first notch adaptation means further comprises .operating weight adaptation means for adjusting variable polynomial coefficients applied to said discrete sampled values to generate said intermediate sequence of discrete values and wherein said step of operating said second notch adaptation means further comprises operating weight adaptation means for adjusting variable polynomial coefficients applied to said intermediate sequence of discrete values to generate said sequence of discrete enhanced values. 35. An apparatus for measuring the mass flow rate of a material in a Coriolis mass flow meter having a flow tube and having first and second sensors associated with the flow tube for generating output signals indicative of the oscillatory movement of said flow tube, said apparatus comprising: analog to digital converter means for periodically sampling sensor output signals at a fixed rate and for converting sampled sensor output signals to digital form to generate a sequence of discrete sampled values representative of said output signals of each of said first and second sensors; digital filtration means, responsive to the generation of said sequence of discrete sampled values, for generating a sequence of discrete enhanced values; phase difference means, responsive to the generation of said sequence of discrete enhanced values, for determining a phase difference between said output signals of said first and second sensors; and mass flow measurement means, responsive to the determination of said phase difference, for determining a mass flow rate value of the material flowing through said flow tube. Flow Meter Description: The present invention relates to mass flow rate measurement and in particular to the use of digital signal processing adaptive filtration methods and apparatus in Coriolis mass flow meters. PROBLEM It is known to use Coriolis mass flowmeters to measure mass flow and other information for materials flowing through a conduit. Such flowmeters are disclosed in U.S. Pat. Nos. 4,109,524 of Aug. 29, 1978, U.S. Pat. No. 4,491,025 of Jan. 1, 1985, and Re. 31,450 of Feb. 11, 1982, all to J. E. Smith et al. These flowmeters have one or more flow tubes of straight or curved configuration. Each flow tube configuration in a Coriolis mass flowmeter has a set of natural vibration modes, which may be of a simple bending, torsional or coupled type. Each flow tube is driven to oscillate at resonance in one of these natural modes. Material flows into the flowmeter from a connected conduit on the inlet side of the flowmeter, is directed through the flow tube or tubes, and exits the flowmeter through the outlet side. The natural vibration modes of the vibrating, fluid filled system are defined in part by the combined mass of the flow tubes and the material within the flow tubes. When there is no flow through the flowmeter, all points along the flow tube oscillate about a pivot point with identical phase due to an applied driver force. As material begins to flow, Coriolis accelerations cause each point along the flow tube to have a different phase. The phase on the inlet side of the flow tube lags the driver, while the phase on the outlet side leads the driver. Sensors are placed on the flow tube to produce sinusoidal signals representative of the motion of the flow tube. The phase difference between two sensor signals is proportional to the mass flow rate of material through the flow tube. A complicating factor in this measurement is that the density of typical process material varies. Changes in density cause the frequencies of the natural modes to vary. Since the flowmeter's control system maintains resonance, the oscillation frequency varies in response to changes in density. Mass flow rate in this situation is proportional to the ratio of phase difference and oscillation frequency. The above-mentioned U.S. Pat. No. Re. 31,450 to Smith discloses a Coriolis flowmeter that avoids the need for measuring both phase difference and oscillation frequency. Phase difference is determined by measuring the time delay between level crossings of the two sinusoidal sensor output signals of the flowmeter. When this method is used, the variations in the oscillation frequency cancel, and mass flow rate is proportional to the measured time delay. This measurement method is hereinafter referred to as a time delay or .DELTA.t measurement. Measurements in a Coriolis mass flowmeter must be made with great accuracy since it is often a requirement that the derived flow rate information have an accuracy of at least 0.15% of reading. The signal processing circuitry which receives the sensor output signals measures this phase difference with precision and generates the desired characteristics of the flowing process material to the required accuracy of at least 0.15% of reading. In order to achieve these accuracies, it is necessary that the signal processing circuitry operate with precision in measuring the phase shift of the two signals it receives from the flowmeter. Since the phase shift between the two output signals of the meter is the information used by the processing circuitry to derive the material characteristics, it is necessary that the processing circuitry not introduce any phase shift which would mask the phase shift information provided by the sensor output signals. In practice, it is necessary that this processing circuitry have an extremely low inherent phase shift so that the phase of each input signal is shifted by less than 0.001.degree. and, in some cases, less than a few parts per million. Phase accuracy of this magnitude is required if the derived information regarding the process material is to have an accuracy of less than 0.15%. The frequencies of the Coriolis flowmeter output signals fall in the frequency range of many industrially generated noises. Also, the amplitude of the sensor output signals is often small and, in many cases, is not significantly above the amplitude of the noise signals. This limits the sensitivity of the flowmeter and makes the extraction of the useful information quite difficult. There is not much a designer can do either to move the meter output signals frequency out of the noise band or to increase the amplitude of the output signals. Practical Coriolis sensor and flowmeter design requires compromises that result in the generation of output signals having a less than optimum signal to noise ratio and dynamic range. This limitation determines the flowmeter characteristics and specifications including the minimum and maximum flow rates which may be reliably derived from the flowmeter's output signals. The magnitude of the minimum time delay that can be measured between the two Coriolis flowmeter output signals at a given drive frequency is limited by various factors including the signal to noise ratio, the complexity of associated circuitry and hardware, and economic considerations that limit the cost and complexity of the associated circuitry and hardware. Also, in order to achieve a flowmeter that is economically attractive, the low limit of time delay measurement must be as low as possible. The processing circuitry that receives the two output signals must be able to reliably measure the time delay between the two signals in order to provide a meter having the high sensitivity needed to measure the flowing characteristics of materials having a low density and mass such as, for example, gases. There are limitations regarding the extent to which conventional analog circuit design can, by itself, permit accurate time delay measurements under all possible operating conditions of a Coriolis flowmeter. These limitations are due to the inherent noise present in any electronic equipment including the imperfections of semi-conductor devices and noise generated by other circuit elements. These limitations are also due to ambient noise which similarly limits the measurement can be reduced to some extent by techniques such as shielding, guarding, grounding, etc. Another limitation is the signal to noise ratio of the sensor output signals themselves. Good analog circuit design can overcome some of the problems regarding noise in the electronic equipment as well as the ambient noise in the environment. However, an improvement in the signal to noise ratio of the output signals cannot be achieved without the use of analog filters. But analog filters alter the amplitude and phase of the signals to be processed. This is undesirable, since the time delay between the two signals is the base information used to derive characteristics of the process fluid. The use of filters having unknown or varying amplitude and/or phase characteristics can unacceptably alter the phase difference between the two sensor output signals and preclude the derivation of accurate information of the flowing material. The flowmeter's drive signal is typically derived from one of the sensor output signals after it is conditioned, phase shifted and used to produce the sinusoidal drive voltage for the drive coil of the meter. This has the disadvantage that harmonics and noise components present in the sensor signal are amplified and applied to the drive coil to vibrate the flow tubes at their resonant frequency. However, an undesirable drive signal can also be generated by unwanted mechanical vibrations and electrical interferences that are fed back to the meter drive circuit and reinforced in a closed loop so that they create relatively high amplitude self-perpetuating disturbing signals which further degrade the precision and accuracy of the time delay measurement. There are several well known methods and circuit designs which seek to reduce the above problems. One such successful technique to reduce some of the above problems is described in U.S. Pat. No. 5,231,884 to M. Zolock and U.S. Pat. No. 5,228,327 to Bruck. These patents describe Coriolis flowmeter signal processing circuitry that uses three identical channels having precision integrators as filters. A first one of these channels is permanently connected to one sensor signal, say, for example, the left. The other two channels (second and third) are alternately connected, one at a time, to the right sensor signal. When one of these, say the second channel, is connected to the right sensor signal, the third channel is connected, along with the first channel, to the left sensor signal. The inherent phase delay between the first and third channel is measured by comparing the time difference between the outputs of the two channels now both connected to the left signal. Once this characteristic delay is determined, the role of this third channel and the second channel connected to the right sensor signal is switched. In this new configuration, the second channel undergoes a calibration of its delay characteristics while the third calibrated channel is connected to the right sensor signal. The roles of second and third channels are alternately switched by a control circuit approximately once every minute. During this one-minute interval (about 30 to 60 seconds), aging, temperature, and other effects have no meaningful influence on the phase-shift of the filters and therefore their phase relationship is known and considered defined. The precisely calibrated integrators used by Zolock provide a signal to noise ratio improvement amounting to about 6 db/octave roll-off in the amplitude transfer function of the integrator. Unfortunately, this 6 db/octave improvement is not enough under many circumstances in which Coriolis flowmeters are operated (such as light material or excessively noisy environments). The reason for this is that a single-pole filter, such as the Zolock integrator, has a relatively wide band width. As a result, noise signals generated by unwanted flow tube vibration modes, noisy environment, material flow noise, electromagnetic or radio frequency interference and disturbances are not removed to the extent necessary for high meter sensitivity required for precision. Depending on their frequency, their amplitude is reduced somewhat, but they can still interfere with the precision time delay measurement between the two sensor output signals when measuring low mass materials such as gases. There is another source for errors in the Zolock and Bruck systems. The integrator time delay measurements are made at three (3) certain well defined points of the sinusoidal sensor signals. The two sensor signals are ideal only when their shape is the same and is symmetrical around their peak values. However, when the two magnetic circuits (sensors) that generate the sensor signals are not identical, the resulting non-ideal wave forms contain different amounts of harmonics with possibly undefined phase conditions which can alter their shape and potentially change their symmetrical character. The result of such variations is such that when, during normal operations, a Zolock integrator is calibrated with one wave form and is subsequently used to measure another wave form, the difference in wave forms may produce an undefined and unknown amount of error due to its harmonic content and its undefined and varying phase of its harmonics. Other analog circuit design techniques suffer from similar problems of complexity, insufficient noise immunity, or insufficient harmonic rejection. There are techniques currently available, such as digital signal processing (hereinafter referred to as DSP) and associated digital filtering, to overcome the above-discussed problems and simultaneously improve the signal to noise ratio of the signals being processed. However, these alternatives have been more complicated and expensive than conventional analog circuit designs. In addition, these prior DSP designs have shown only modest improvement over conventional analog circuit designs with respect to noise immunity and harmonic rejection. U.S. Pat. No. 4,934,196, issued Jun. 19, 1990 to Romano, teaches a DSP design for computing the phase difference, .DELTA.t, and correlated mass flow rate. Romano's design alters the sampling frequency of an A/D converter in an attempt to maintain an integral number of sample times within each periodic cycle of the vibrating flow tubes. This need for variable frequency sampling complicates Romano's DSP design. Although this DSP design is structurally quite distinct from prior discrete analog circuit designs, it has proven to provide only modest improvements over analog designs in measurement accuracy because it provides significant improvement in filtration only at integer multiples of the fundamental frequency. However, many signal components result from mechanical vibration modes of the flow tubes whose resonant frequencies are not integer multiples of the fundamental frequency and are therefore poorly rejected by the prior DSP designs. Neither prior approach (analog nor prior DSP) effectively rejects non-harmonic or broadband noise. From the above discussion, it can be seen that there is a need for an improved method and apparatus for measuring mass flow rate in a Coriolis mass flow meter. SOLUTION The present invention solves the above identified problems and achieves an advance in the art by applying digital filtering and digital signal processing (DSP) methods and apparatus to improve the accuracy of mass flow measurements in a Coriolis mass flow meter. The present invention comprises a DSP design which includes adaptive notch filters to improve the accuracy of frequency and phase measurements used in the computation of mass flow rate. The use of adaptive notch filtration in the present invention is one application of the technology commonly referred to as Adaptive Line Enhancement (ALE). In the present invention, the signal from each vibrating flow tube sensor is sampled, digitized, and then processed by a digital adaptive notch filter which passes all noise signals outside a narrow frequency band (a notch) around the fundamental frequency. This digitized filtered signal is then subtracted from the original digitized signal to produce an enhanced signal representing the sensor output signal waveform at the fundamental frequency with virtually all noise signals eliminated. This method and apparatus eliminates harmonic as well as non-harmonic noise signals. Initially the width of the notch filter's "notch" is wide and is adapted over time to narrow as it converges on the fundamental frequency. Adaptation algorithms rapidly adapt the notch frequency of the adaptive filter to track changes over time in the fundamental frequency of the vibrating flow tubes. The DSP design of the present invention uses a fixed sampling frequency as distinct from Romano's variable frequency design. This fixed sampling frequency approach permits rapid convergence of the adaptive notch filters on the fundamental frequency of the vibrating flow tubes and simplifies the total circuit design. The fixed sampling rate eliminates the need exhibited in Romano to provide additional circuitry to vary the sampling rate. The present design performs computational adjustments to compensate for spectral leakage between the fixed sampling frequency and the variable fundamental frequency of the vibrating flow tubes. Despite this added computational complexity, the present invention is simpler than prior designs exemplified by Romano and provides better noise immunity due to the use of adaptive notch filtration. The present invention provides superior noise immunity and harmonic rejection as compared to all known designs and simplifies aspects of the DSP design disclosed by Romano. This permits improved accuracy of the flow rate measurements even in particularly noisy environments as well as applications with low density flow materials (such as gas). Since the flow tubes vibrate at the same fundamental frequency, adaptation of the notch filters is determined by samples from only one of the two notch filters. The adaptation weights so determined are applied to both notch filters. Heuristics applied to the computations by the present invention prevent the notch filters from diverging from the fundamental frequency due to instability in the computations. Other heuristics restart convergence computations for the adaptation when the signal to noise ratio measured by the notch filter is too small. A small signal to noise ratio indicates that the adaptive notch filter is not converged on the fundamental frequency. This may be due to a shift in the fundamental frequency of the vibrating flow tubes. In a first embodiment of the present invention, the output signal from each vibrating flow tube sensor is sampled at a fixed frequency by a corresponding A/D converter. The sampled value generated by each A/D converter is then applied to a corresponding decimation filter to reduce computational complexity by reducing the number of samples used in subsequent computations. The decimation filters also provide a degree of anti-aliasing filtration to smooth the sampled analog signals. The decimated signals are then each applied to a corresponding adaptive notch filter to further enhance the signal from each sensor. The enhanced output signal from each sensor, after being filtered of most noise and harmonics, is then applied to a corresponding phase computation element to determine the phase difference between the two enhanced signals. The output of each phase computation element is applied to a computation element to determine the time difference between the enhanced sensor signals and hence the proportional mass flow rate. In a second embodiment of the methods of the present invention, four adaptive notch filters are utilized, two in series on each of the left and right channel signals. The two filters on each of the left and right channels are "cascaded" in that the first filter utilizes a low-Q (wide notch) filter to supply limited signal enhancement but the ability to rapidly converge on changes in the fundamental frequency of the vibrating flow tubes. The signal output from the first cascaded notch filter is then applied to a second cascaded notch filter. The second notch filter utilizes a high-Q (narrow notch) filter to provide superior noise and harmonic rejection over that of previous solutions or over that of the first embodiment described above. Despite the narrow notch (high-Q) of the second notch filter, it can still rapidly adapt to changes in the fundamental frequency of the vibrating flow tubes due to the limited enhancement (filtration) performed by the first notch filter. The reduced noise and harmonic levels in the signal applied to the second notch filter allow it to also rapidly converge on changes in the fundamental frequency of the vibrating flow tubes. An additional notch filter (fifth filter) having a notch shape even wider than that of the first cascaded notch filter is used to provide an estimate of the fundamental frequency of the vibrating flow tubes. This estimate is used by weight adaptation computations to set the frequency parameter of the first cascaded notch filters for both the left and right channels. The output from the second cascaded notch filters is used by weight adaptation computations to adjust the frequency parameter of the second cascaded notch filters. This combination of two (or more) cascaded adaptive notch filters to enhance the output signal from each sensor further enhances both the rejection characteristics of the filtration and the speed with which the adaptive filters converge on changes in the fundamental frequency of the vibrating flow tubes. The term "adaptive notch filter" as used herein refers broadly to a filter with variable parameters. This definition contrasts with a more widely accepted definition which combines a variable parameter filter with a mechanism for automatically tuning the parameters of the filter based on the filter's own inputs and outputs. As used herein, the adaptation of some notch filters is computed based on the operation of other filters rather than each filters own inputs and outputs. In other words, some notch filters in the present invention are slaved to the operation of other notch filter computations. For this reason, the detailed discussions of the filters and the adaptation mechanisms are separated. One adaptation computation may adjust the parameters for multiple notch filters based on inputs from a single filter. The above and other aspects of the present invention will become apparent from the following description and the attached drawing. BRIEF DESCRIPTION OF THE DRAWING FIG. 1 shows a typical Coriolis mass flow meter attached to meter electronics which embody the apparatus and methods of the present invention; FIG. 2 shows a block diagram of the computational elements within the meter electronics which determine mass flow rate through the flow meter in accordance with the present invention; FIG. 3 shows additional detail of a first embodiment of the present invention shown in FIG. 2 wherein a single adaptive notch filter is used in conjunction with each sensor signal; FIGS. 4-12 show additional detail of the computational elements of the first embodiment of the present invention shown in FIG. 3; FIG. 13 shows additional detail of a second embodiment of the present invention shown in FIG. 2 wherein two cascaded adaptive notch filters are used in conjunction with each sensor signal; FIGS. 14-16 show additional detail of the computational elements of the second embodiment of the present invention shown in FIG. 13; FIG. 17 is a flowchart of a software implementation of the first embodiment of the present invention and depicts interrupt processing for servicing of an A/D converter and associated decimation of the samples; FIG. 18 is a flowchart of a software implementation of the first embodiment of the present invention and depicts processing of decimated samples for purposes of filtering and determination of .DELTA.t phase difference; FIG. 19 is a flowchart depicting additional detail of an element of FIG. 18 which determines updated filter parameters after each decimated sample is processed; and FIG. 20 is a block diagram of digital signal processing electronics suitable to perform the software methods of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT A typical Coriolis mass flowmeter 10 is illustrated in FIG. 1 as having two cantilever mounted flow tubes 12, 14 affixed to a manifold body 30 so as to have substantially identical spring constants and moments of inertia about their respective out-of-phase bending axes W--W and W'--W'. A drive coil and magnet 20 are mounted at a midpoint region between the top portion 130 and 130' of flow tubes 12, 14 to oscillate flow tubes 12, 14 out of phase about axes W--W and W'--W'. Left sensor 16 and right sensor 18 are mounted near the respective ends of the top portions of flow tubes 12, 14 to sense the relative movement of flow tubes 12, 14. This sensing may be done in many ways including by measuring the movement of the top ends of the flow tubes 12, 14 through their zero crossings or some other pre-defined point. Flow tubes 12 and 14 have left side legs 131 and 131' and right side legs 134 and 134'. The side legs converge downwardly toward each other and are affixed to surfaces 120 and 120' of manifold elements 121 and 121'. Brace bars 140R and 140L are brazed to the legs of flow tubes 12, 14 and serve to define the axes W--W and W'--W' about which the flow tubes oscillate out of phase when driver 20 is energized over path 156. The position of axes W--W and W--W' is determined by the placement of brace bars 140R and 140L on flow tube side legs 131, 131' and 134, 134'. Temperature detector 22 is mounted on side leg 131 of flow tube 14 to measure the flow tube's temperature and the approximate temperature of the material flowing therein. This temperature information is used to determine changes in the spring constant of the flow tubes. Driver 20, sensors 16 and 18 and temperature detector 22 are connected to mass flow instrumentation 24 by paths 156, 157, 158 and 159, respectively. Mass flow instrumentation 24 includes at least one microprocessor which processes the signals received from sensors 16, 18, and 22 to determine the mass flow rate of the material flowing through flowmeter 10 as well as other measurements, such as material density and temperature. Mass flow instrumentation 24 also applies a drive signal over path 156 to driver 20 to oscillate tubes 12 and 14 out-of-phase about axes W--W and W'--W'. Manifold body 30 is formed of casting 150, 150'. Casting elements 150, 150' are attachable to a supply conduit and exit conduit (not shown), by flanges 103, 103'. Manifold body 30 diverts the material flow from the supply conduit into flow tubes 12, 14 and then back into an exit conduit. When manifold flanges 103 and 103' are connected via inlet end 104 and outlet end 104' to a conduit system (not shown), carrying the process material to be measured, the material enters manifold body 30 and manifold element 110 through inlet orifice 101 in flange 103 and is connected by a channel (not shown) having a gradually changing cross-section in casting element 150 to flow tubes 12, 14. The material is divided and routed by manifold element 121 to the left legs 131 and 131' of flow tubes 14 and 12, respectively. The material then flows through the top tubes elements 130, 130' and through the right side legs 134 and 134' and is recombined into a single stream within flow tube manifold element 121'. The fluid is thereafter routed to a channel (not shown) in exit casting element 150' and then to exit manifold element 110'. Exit end 104' is connected by flange 103' having bolt holes 102' to the conduit system (not shown). The material exits through outlet orifice 101' to return to the flow in the conduit system (not shown). Mass flow instrumentation 24 analyzes signals received on paths 157, 158, and 159 and generates standard output signals on path 155 to indicate mass flow rates utilized by a control system or operator for monitoring and control of the mass flow rate through the associated conduit system (not shown). OVERVIEW The present invention comprises digital signal processing methods operable within a digital signal processor (DSP) chip to perform the computational functions within mass flow instrumentation 24. Discrete samples are taken of the analog signals generated as output from each of the flow tube sensors. The discrete samples from the left and right sensors are digitized by use of standard analog to digital conversion (A/D) devices. Once digitized, further processing of the samples is performed by digital signal processing methods within the DSP chip. The processing of the digitized signal samples is expressed herein in two forms. In one form of expression, the DSP software flowcharts and equations used for the various filtering and processing functions are presented. To aid in the explanation of the methods of the present invention, a second form of expression is utilized which depicts the computation of the various equations as pseudo-circuits (e.g. block diagrams representing summing junctions, multiplication junctions, delay circuits, registers, multiplexors, etc.). Certain more complex mathematical operations are left as high level elements in the pseudo-circuit diagrams and are typically referred to herein as "computational elements". The two forms of explanation of the present invention are intended as equivalent descriptions, either of which fully specifies the methods and function of the present invention. OVERVIEW- PSEUDO CIRCUITS FIG. 2 depicts the general structure of, and associated flow of information in, the flow meter electronics of the present invention. The meter electronics of the present invention is comprised of two essentially identical "channels": a first channel for processing the left flow tube sensor output signal and a second channel for processing the right flow tube sensor output signal. The two "channels" are identical except with respect to the weight adaptation of the notch filters as discussed below. The description presented below is discussed in terms of a typical Coriolis flowmeter application in which the fundamental frequency of the vibrating flow tubes is approximately 100 Hz. It will be readily recognized that the apparatus and methods of the present invention may be applied to any common flowmeter fundamental vibrating frequency. Many of the computational elements discussed below operate synchronously with clock signals associated with various samplings of the flow tube sensor output signals. CLOCK 214 of FIG. 2 provides clocking signals associated with the various sampling rates of the computational elements discussed below. First, CLOCK 214 supplies a periodic pulsed signal clock to A/D converters 200 over path 270 to determine the sampling rate of the raw (unprocessed) signals generated by the flow tube sensors. Each A/D converter 200 samples its corresponding analog signal and converts the sampled value to digital form once for each signal pulse applied to path 270 by CLOCK 214. This clock signal applied to A/D converters 200 over path 270 must have a highly accurate frequency to permit sampling of the flow tube sensor output signals at a fixed sampling rate as required for the processing of the present invention. This clock pulse accuracy is preferably achieved by use of a crystal controlled clock. This same clock signal is also applied to 48:1 decimation filter elements 202 via path 270. The decimation filter elements 202 reduce the number of samples by a factor of 48 while providing significant anti-aliasing filtration of the sampled signal values. One of ordinary skill in the art will recognize that the particular decimation ratio of 48:1 is a matter of engineering design choice depending upon the particular application environment. CLOCK 214 also provides a signal CLK to other computational elements discussed below. The frequency of the CLK signal corresponds to the frequency of sample values output by the decimation filter elements 202. In other words, the frequency of the CLK clock signal is 1/48th the frequency of the clock signal generated and applied to path 270. In the preferred embodiment of the present invention, the computational elements "clocked" by the CLK signal are implemented as software functions operable on a digital signal processing (DSP) chip. As such, these functions perform their computations on the decimated discrete sampled sensor output signal values. The "clocking" of these functions corresponds to the availability of discrete sampled values. These values are preferably buffered in software implemented queues or FIFOs so that the functions may actually operate asynchronously with respect to the fixed rate, crystal controlled, sampling frequency of the A/D converters 200. In the description of the figures which follow, the CLK signal is representative of the frequency at which decimated, discrete, sampled sensor output signal values are made available for further processing by the computational elements. The actual computation processing in software within the DSP chip proceeds generally asynchronously with respect to the A/D sampling frequency of the clock signal on path 270. The output signal from the right flow tube sensor 18 of FIG. 1 is applied to A/D converter 200 over path 158 of FIG. 1. The output signal from the left flow tube sensor 16 of FIG. 1 is applied to a second A/D converter 200 over path 157 of FIG. 1. A/D converter 200 samples and converts the analog signal from the right flow tube sensor to a digital value. A second A/D converter 200 samples and converts the analog signal from the left flow tube sensor to a digital value. A/D converters 200 operates responsive to the fixed frequency periodic clock signal received on path 270 supplied by a system wide clock 214. The converted digital value is applied over path 252 to 48:1 decimation filter element 202. The 48:1 decimation filter element 202 is done in two stages, an 8:1 stage followed by a 6:1 stage. Both stages of decimation filter element 202 are preferably implemented as finite impulse response (FIR) anti-aliasing filters. One skilled in the art will recognize that an IIR filter may be used for the decimation stages. Use of FIR versus IIR filtration is a matter of design choice based on computational complexity and the relative power of the computational elements used in a particular design. The first stage of decimation filter element 202 performs an 8:1 reduction in the sample rate from 38.4 kHz to 4.8 kHz. The transfer function of the filter is: G(z)=(1-z.sup.-8).sup.5 /(1-z.sup.-1).sup.5 Pole-zero cancellation yields an FIR filter of 36 taps. The filter has 5 zeros at each multiple of the subsampling frequency. This provides strong rejection of those frequencies which alias into the passband of the second stage filter. This first stage filter has small integer coefficients which may be represented in single precision computer arithmetic to thereby simplify computational complexities of the convolution and improve execution speed. The second stage filter of the decimation filter element 202 performs a 6:1 reduction in sample rate from 4.8 kHz to 800 Hz. The second stage filter is a 131 tap FIR filter designed using the well known Remez exchange algorithm. The passband is DC through 250 Hz and the stopband begins at 400 Hz. The passband has weight 10.sup.-5 and the stopband has weight 1. A high degree of anti-aliasing is provided by the two stage decimation filters. All aliasing components are reduced by over 120 dB, while ripple from DC through 230 Hz is less than 1.5 dB. The left channel, comprising A/D converter 200 and decimation filter element 202 connected via path 250, operates identically to the above-discussed right channel. The output of decimation filter element 202 for the left channel applies its output signal to path 254. The sample values from A/D converters 200 and the computations of the decimation stages preferably utilize 32-bit fixed point arithmetic to maintain the computational accuracy and performance required. Subsequent computations for the notch filtration, phase computations, .DELTA.t computations, and mass flow rate computations are preferable performed using floating point arithmetic due to the wider range of computational scaling involved with the more complex functions. The anti-aliased, decimated, digitized signal values are applied over path 256 to adaptive notch filter 204. Adaptive notch filter 204, discussed in detail below, enhances the signal values by effectively filtering all frequencies outside a band centered about the fundamental frequency of the vibrating flow tubes. The adaptive notch filter 204 eliminates a band of frequencies (a notch) centered about the fundamental frequency. The resultant signal is all noise outside the notch centered about the fundamental frequency of the vibrating flowtubes. This noise signal is then subtracted from the signal applied as input to the notch filter 204 over path 256 which is the sum of the fundamental frequency and all noise not filtered by decimation filter element 202. The result of the subtraction, which represents the fundamental frequency of the vibrating flowtubes filtered of most noise signals, is then applied to path 262 as the output of the notch filter 204. The parameters (weighting factors or coefficients and the debiasing parameter) of the notch filter 204 determine the characteristics of the notch, namely the shape of the notch (bandwidth of frequencies rejected) and the fundamental frequency. The parameters are computed by weight adaptation element 210 and applied to notch filter 204 over path 258. The left channel adaptive notch filter 204 accepts its input over path 254 and applies its output to path 260. As discussed below, signals generated as outputs from left channel adaptive notch filter 204 are used by weight adaptation element 210 as feedback in determining the coefficients of both notch filters (left and right channel adaptive notch filters). The weighting factors (coefficients) of both notch filters 204 (left and right signal channels) are determined by operation of weight adaptation element 210. Weight adaptation element 210 receives the filtered signal, the noise portion of the unfiltered signal, and a gradient of the filtered signal from the output of left channel adaptive notch filter 204. These signal values are used in the time-dependent (iterative) computations to determine the appropriate coefficients of the notch filters. The coefficients so determined control the characteristics of the notch. Both the shape of the notch and the fundamental frequency are adapted to track changes in the fundamental frequency. The shape of the notch determines the speed with which the adaptive notch filters can converge on changes to the fundamental frequency. A wider notch provides less filtration but may be more rapidly adjusted to changes in the fundamental frequency. A narrower notch converges more slowly to changes in the fundamental frequency but provides superior filtration of the input sensor signals. It will be recognized that either the left or right channel output signals may be used as feedback to the weight adaptation element 210. Though it would be possible to utilize both the left and right channel output signals in weight adaptation element 210, there is no clear benefit in so doing to outweigh the added computational complexities. Regardless of the source of inputs to weight adaptation element 210, the weight adaptation parameters computed therein are applied to both the left and right channel adaptive notch filters 204 so that both sensor signal output channels are processed identically. Using a single set of parameters applied to both the left and right channels serves to maintain the critical phase relationship between the two channels, the fundamental value used to compute the .DELTA.t value proportional to mass flow rate. The values computed by the weight adaptation element 210 are also used, as discussed below, in the phase and .DELTA.t computations. Element 212 receives coefficients from weight adaptation element 210 and determines the fundamental frequency of the vibrating flow tubes. Frequency and Goertzel weight information are generated by frequency calculation element 212 and applied to path 268. The filtered signal values generated by adaptive notch filter 204 are applied to phase computation element 206 over path 262. Phase computation element 206 receives Goertzel weight and frequency information over path 268 from frequency calculation element 212. Phase computation element 206 uses Fourier analysis techniques with two Hanning windows to determine the phase of the filtered signal. The length of a window is a function of the nominal or expected flow tube fundamental frequency. The length of a window determines a number of oscillatory cycles of the flow tubes over which samples are gathered and weighted to determine the phase of the flow tubes. The expected flow tube frequency may be programmed into the electronics of the present invention at time of manufacture, or may be entered as a parameter at a particular installation/application site, or may be determined by operation of the flowmeter and appropriate measurements. The length of a window represents a tradeoff between response time and rejection of signal noise and leakage. A larger number of cycles accumulated to determine the phase provides for additional rejection of noise but requires additional delay to achieve causality and therefore slows response to changes in the flow tube vibration phase relationship. Fewer samples reduces the delay and therefore improves the speed of response to flow tube vibration phase changes, but provides inferior noise rejection. Eight flow tube cycles is selected as the preferred window length as measured in cycles. Assuming a given expected frequency, the preferred window size (2N) is determined as: window.sub.-- length=2. floor(3200/expected.sub.-- tube.sub.-- frequency) where floor(x) is the largest integer less than or equal to x. The Hanning window is represented as a vector of weights to be applied to the discrete samples over the period of one Hanning window. Where 2N is the number of discrete samples within one period of the Hanning window, the weight for the k'th discrete sample where k ranges from 0 to 2N-1 is determined as: h(k)=(1/2) (1-cos (2.pi.k/(2N-1))) A haft window signal pulse is generated by CLOCK 214 of FIG. 2 and applied to path 274 of FIG. 2. every N discrete samples (where a complete Hanning window of the sampled sensor output signal has 2N discrete samples in a single period) for purposes discussed in detail below relating to parallel computations of overlapping Hanning windows. In addition, CLOCK 214 of FIG. 2 applies SAMPNO, a counter value, on path 272. SAMPNO on path 272 counts (as a modulo N function of the CLK signal) from 0 to N-1. The SAMPNO counter on path 272 increments with each pulse of the CLK signal. When SAMPNO reaches N-1 the next pulse of the CLK signal from CLOCK 214 resets SAMPNO to 0. The half window signal corresponds to the SAMPNO counter being equal to zero. In the preferred embodiment of the present invention, the SAMPNO counter is implemented in software which counts the number of discrete decimated sampled sensor output signal values processed during a Hanning window. The software implementation of the SAMPNO counter increments asynchronously with respect to the fixed frequency, crystal controlled clock provided by CLOCK 214 of FIG. 2 on path 270. The signal samples at the edges of each window are given lower weights than those toward the middle of the window. To more fully utilize the available data, two Fourier calculations are done simultaneously such that the windows overlap by one half of a window length. New Fourier phase measurements are produced every half window of samples. The use of a constant window size in the present invention allows the Hanning window weights to be pre-computed before flow measurements begin. When used in conjunction with a discrete-time Fourier transform (DTFT), as in the present invention, the window size determines the sharpness of the frequency discrimination characteristic of the DTFT filter output. It also increases the rejection of noise and pseudo-harmonics. Unfortunately, a longer window size provides slower response of the filter to changes in phase. The window size as determined above therefore represents the best known approximation suited to balancing these competing goals (improved frequency discrimination and noise rejection versus rapid response to phase changes). The preferred window size may be changed for different flow meter applications to optimize for certain environmental conditions. Phase computation elements 206 sum the filtered discrete sampled values to generate a complex number indicative of the phase of the sampled, filtered sensor output signal. This complex number is applied to path 266 to be used in subsequent .DELTA.t computations. Specifically, a Goertzel filter Fourier transform is applied to each Hanning window of filtered, discrete sampled sensor output signal values of both the right and left channels. The coefficients of the Goertzel filter are determined by the frequency computation element 212 and supplied to phase computation elements 206 over path 268. The complex number output of phase computation element 206 is applied to path 266 and is used by the .DELTA.t computation. The phase computation element 206 for the left channel operates identically to the above-discussed right channel. The output of adaptive notch filter 204 for the left channel applies its output signals to path 260. Phase computation element 206 receives these signals and applies values indicative of the phase of the left channel signal to path 264. Phase information for both the left and right channels is determined by operation of phase calculation elements 206 and received by .DELTA.t calculation element 208 over path 264 for the left channel and path 266 for the right channel. Frequency information determined by operation of frequency calculation element 210 is received by .DELTA.t calculation element 208 over path 268. .DELTA.t calculation element 208 determines the time delay resultant from the phase difference between the left and right sensor output signals, which in turn is approximately proportional to the mass flow rate of the material flowing through the flow tubes of the Coriolis flowmeter. The Fourier transform of the left channel is multiplied by the conjugate of the Fourier transform of the right channel. The angle of the complex result is then computed. This phase difference angle is divided by the tube frequency of the vibrating flow tubes (converted to appropriate units to match the phase measurements) to produce a .DELTA.t value. OVERVIEW - SOFTWARE The flowcharts of FIGS. 17-19 provide an overview of the operation of a software implementation of the methods of the present invention. FIG. 17 depicts the operation of a portion of the software which operates in real time in response to an interrupt from the A/D converters 200 (of FIG. 2). FIG. 18 depicts the operation of a portion of the software implementation which performs further filtration and processing on the decimated samples produced by operation of the software depicted in FIG. 17. Decimated samples produced by operation of the software depicted in FIG. 17 are buffered so that the software of FIG. 18 may operate asynchronously with respect to the accurately timed samples from the A/D converters 200. FIG. 19 provides additional detail of an element in FIG. 18 which includes heuristic methods to help assure stability and accuracy of the resultant measurements of mass flow rate. The software of FIGS. 17-19 is operable on mass flow instrumentation 24 shown in greater detail in FIG. 20. Digital signal processor 2000 of FIG. 20 is a computing device much like any common microprocessor but with special purpose functions tuned for application to signal processing tasks. Many such DSP processor devices are known to those skilled in the art. One example of such a device is the Texas Instruments TMS 320C50-57. This device is a fixed point arithmetic signal processor. Software emulation libraries are provided for precision floating point computations. This exemplary device provides 32-bit precision required for the sampling and decimation operations. The floating point emulation software provides adequate performance for most flow meter applications though other processor devices may be used if additional floating point computational performance is required for a particular flow meter application. Processor 2000 reads program instructions from program ROM 2002 over bus 2052 and manipulates data and buffers in RAM 2004 over bus 2054. One of ordinary skill will recognize that, depending upon several cost and performance factors, it may be preferable under certain circumstances to copy the program instructions from ROM 2002 to RAM 2004 to improve the performance of processor 2000 in fetching instructions. A/D converters 200 each receive an analog signal from their respective flow tube sensor output signals applied to paths 157 and 158, respectively. Processor 2000 applies control signals to A/D converters 200 over paths 250 and 252, respectively, and receives digitized sample values from the A/D converters 200 over paths 250 and 252, respectively. Processor 2000 applies control signals over path 2050 to clock 214 to determine the sampling frequency of A/D converters 200. In response, clock 214 applies a sample frequency clock signal to A/D converters 200 over path 270. In this manner, processor 200 initially sets the sample frequency of A/D converters 200 to the desired rate. In the preferred embodiment, A/D converters 200 are embodied within a single integrated circuit with multiple converters and a single communication bus connection to the DSP processor. This helps assure that the phase relationship between the two sampled signals is due to the Coriolis effects of the vibrating flow tubes rather than effects of imbalances between physically separate A/D converter circuits. Many such stereo A/D converter chips are known to those skilled in the art. One example of such a chip is the Crystal Semiconductors CS5329, a 2-channel stereo A/D converter device. Processor 2000 determines the appropriate fundamental frequency at which the flow tubes are vibrated and applies a proportional signal to path 2058. Driver circuit 2008 converts the signal applied to path 2058 into a signal appropriate to drive the flow tubes to vibrate and applies the signal to path 156. Many methods and apparatus to drive the flow tubes to vibrate are well known in the art and need not be discussed here in further detail. Processor 2000 also determines a .DELTA.t value from the phase difference between the sampled channels and applies a signal proportional to .DELTA.t to path 2056. D/A converter 2006 converts the signal value applied to path 2056 into an analog signal applied to path 155 proportional to mass flow rate. The signal on path 155 is applied to utilization means (not shown) appropriate to the particular flow meter measurement application. OVERVIEW - SOFTWARE (REAL TIME INTERRUPT PROCESSING) As noted above, the A/D converters 200 operate at a fixed frequency to provide accurately timed sample values of the sensor output signals from the left and right flow tubes. As shown in FIG. 17, the raw sample values are decimated by a two-stage, 48:1 decimation filter. The decimation filtration provides some smoothing (anti-aliasing) of the sampled data while reducing the sample rate and thus the computational power required to apply the notch filters and to determine phase differences and the resulting .DELTA.t measurement. Well known software techniques may be applied to permit the nesting of interrupts during certain less critical computational processing to thereby avoid any possible loss of data due to complex computations while an A/D converter 200 sample interrupt is being processed. For example, circular buffering as in the use of FIFO memory techniques can be applied to retain additional data while previous samples are being processed. These buffering techniques and others are well known to those of ordinary skill in the art and need not be addressed further. Element 1700 of FIG. 17 represents the occurrence of an interrupt generated by the A/D converters 200 to signify the availability of a digitized sample for both each of the left and right flow tube sensor signal outputs. Elements 1702 then operates in response to the interrupt to read the sampled, digitized values from the A/D converters 200 for each of the left and right flow tube sensor signals (also referred to herein as the left and right channels). The sampled, digitized values read from the A/D converters 200 are stored in a first stage circular buffer associated with each of the left and right channels. Each channel's first stage circular buffer is of sufficient size to store the sampled values of the FIR filter. The first stage filter is preferably a 36 tap filter and therefore requires at least 36 entries in the circular buffer for each channel. Element 1704 is operable to determine if eight new samples have been stored in the first stage circular buffer since the last convolution of the sample values read from the A/D converters 200 by operation of element 1702. If eight new samples have not yet been so read, then processing of this A/D converter 200 interrupt is complete. If eight new samples have been stored in the first stage circular buffer since the last filter convolution, then element 1706 is operable to determine the convolution of the 36 sampled values currently stored in the first stage circular buffer for each channel. The convolved value for each channel is then stored in a second stage circular buffer associated with each channel. Each channel's second stage circular buffer is of sufficient size to store the sampled values of the FIR filter. The second stage filter is preferably a 131 tap filter and therefore requires at least 131 entries in the circular buffer for each channel. Element 1708 is operable to determine if six new values have been stored in the second stage circular buffer by operation of element 1706. If six new values from the first stage convolution have not yet been stored in the second stage circular buffer, then processing of this A/D converter 200 interrupt is complete. If six new values have been stored in the second stage circular buffer, then element 1710 is operable to determine the convolution of the 131 values stored in the second stage circular buffer for each channel. The second stage filter sum (convolution) of the second stage circular buffer values for each channel is then stored in a decimated sample circular buffer associated with each channel. Each channel's decimated sample circular buffer holds decimated values for its associated left or right channel samples. The buffers are used to hold the decimated values until the asynchronous processing described below with respect to FIG. 18 can retrieve the values for further filtering and processing. The decimation computations are simple enough that they can be processed within the interrupt processing software of this FIG. 17. Further processing to apply the notch filter, to determine phase difference and .DELTA.t values, and to adapt the notch filter parameters, is more complex and therefore operates asynchronously with respect to the real time processing required for reading sample values from the A/D converters 200. One of ordinary skill in the art will recognize that the division of tasks between the interrupt processing of FIG. 17 and the asynchronous processing of FIG. 18 is a matter of design choice depending upon the performance characteristics of the selected DSP chip and desired performance goals as measured by A/D converter sampling frequency. A number of equivalent software and associated data structures are within the spirit and scope of the present invention. The software structure summarized herein with reference to FIGS. 17-19 are described below in "pseudo-circuits" to aid in understanding of the present invention. In these pseudo-circuit descriptions, a signal referred to as CLK is pulsed for each decimated sample generated by the operations described above in FIG. 17. In other-words, the CLK signal is 1/48th the sample frequency. As can be seen in the software description depicted in FIGS. 17-19 the CLK signal indicates simply that a decimated sample value is available in the decimated sample circular buffers (more precisely a pair of decimated values, one for the left channel and one for the right). The computationally more complex notch filtration and .DELTA.t determinations are performed asynchronously with respect to the accurately timed sample frequency clocked A/D conversions and associated two-stage decimations. In other words the CLK signal discussed below is preferably no more than an indication that a decimated sample is available in the decimated sample circular buffer. OVERVIEW - SOFTWARE (ASYNCHRONOUS DIGITAL SIGNAL PROCESSING) FIG. 18 is a flowchart depicting the asynchronous portion of the software which is operable in response to the real time sampling and decimation operations discussed above with respect to FIG. 17. Element 1800 of FIG. 18 represents all processing required to initialize the circular buffers (first stage, second stage, and decimated sample) used to pre-process the sampled data for both channels. In addition, element 1800 initializes any required hardware associated with the A/D converters 200 of FIG. 2 to setup the fixed sampling frequency of the converters (i.e. clock 214) and to enable the A/D converters 200 to interrupt the DSP operation when a sampled value is available from the A/D converters 200. Element 1802 is operable to wait until a pair of decimated sample values is available in each of the decimated sample circular buffers (one for the left channel and one for the right). When a pair of decimated sample values is available element 1804 is operable to apply the notch filter function to the decimated, sampled value to thereby enhance the signal. The signal is enhanced by removing unwanted noise and harmonics of the signals frequency. Element 1806 is next operable to update the parameters of the notch filters. The adaptation methods of the present invention adapt the notch filter parameters to account for changes in the fundamental frequency of the vibrating flow tubes. In the process of the notch filter adaptations, heuristics are utilized to help assure stability of the flow measurements made by meter instrumentation 24. These heuristics are discussed in further detail below. The updated filter parameters are applied to the notch filters. Element 1812 of FIG. 18 is next operable to determine if the sample is the first sample at the start of a new half window period (i.e. SAMPNO=0 indicating that all samples in the previous half window have been processed). If the sample is not the first sample at the start of a half window period, then processing continues with elements 1808 and 1810 to update the Goertzel filter parameters and to accumulate the signal and noise energy values. If the sample is the first sample in a new half window period, then processing which relates to completion of the previous half window is performed by operation of element 1814 discussed below. Element 1814 is operable at the end of a half window period (the start of a new half window period) to determine the signal to noise ratio (SNR) given the accumulated enhanced sample energies and accumulated enhanced noise component energies generated by operation of element 1810 discussed below. The accumulated energy sums generated by operation of element 1810 are also reset by operation of element 1814 to prepare the accumulation for the start of the next Hanning half window period of samples. Element 1816 then tests whether the SNR is above an acceptable threshold. In the present invention, a preferred SNR threshold for many common applications is five. One of ordinary skill in the art will recognize that the preferred SNR threshold may vary according to the needs of each particular flow measuring environment and application. If the SNR value drops below the predetermined threshold value then an SNR fault condition is said to exist for the previous half window period (the just completed half window). If element 1816 determines that there was an SNR fault in the previous half window, then processing continues with element 1818. Otherwise, processing continues with element 1820. Element 1818 is operable to reset the computations involved in the weight adaptation of the notch filters. Specifically, the debiasing parameter (.alpha.), the forgetting factor (.lambda.), and the covariance matrix (P) are all reset to states which restart the computations to converge the notch filter on the fundamental frequency of the vibrating flow tubes. Element 1820 is next operable to determine .DELTA.t from the complex numbers indicative of phase of the signal on each channel during the period of the immediately preceding sample values. In other words, after each Hanning window of sample values (which occurs every half window as discussed below), a .DELTA.t value is computed from the immediately preceding Hanning window samples reduced to a complex number indicative of phase for each channel. Element 1820 is further operable to determine the Goertzel filter coefficients for the next period from the accumulated parameters generated by element 1808. The parameter accumulation of element 1808 is also reset to begin a new period. Processing then continue with elements 1808 and 1810 to update the Goertzel filter parameters and accumulate the signal and noise energies. Element 1808 is operable to update the Goertzel filter by accumulating the average notch filter weights over a half window period. At boundaries of the half window periods, the Goertzel filter weights are updated in preparation for processing of the samples during the next half window period. Element 1808 is also responsive to the generation of the enhanced sampled values and applies the enhanced sample values to a complex Goertzel filter. The Goertzel filter, as discussed above, produces a complex number, accumulated over a series of waveform sample values, representative of the phase of the waveform. This phase value is accumulated for both the left and right channels. As discussed above, the Goertzel filters are used to accumulate a complex number indicative of the phase of the enhanced sampled signal of each channel. The accumulation continues through a number of samples equal to the length of a Hanning window (said length denoted 2N). The samples in a Hanning window approximately span eight full vibration cycles of the associated flow tube sensor signal. To maximize the utilization of the sampled data, two Goertzel filter computations are performed in parallel on the samples of a channel (totalling four computations, 2 each on the left and right channel). The two parallel computations on a channel are performed on the same enhanced sample values of the channel but one computation begins one half Hanning window length after the other (i.e. delayed by N samples). In other words, the two parallel Goertzel filter computations applied to samples of a channel are separated from one another in time by the one half the Hanning window period of the vibrating flow tube sensor signal samples. Element 1810 is operable to accumulate the enhanced signal energy and to accumulate the noise energy of the sampled values. The accumulated values are checked at the end of a half window (as discussed above with respect to element 1814) to determine if the signal to noise ratio is within desired limits. Processing of the method then continues by looping back to element 1802 to await the receipt of another decimated sample value. FIG. 19 provides additional detail of the operation of element 1806 which updates the filter parameters in preparation for processing another decimated sample value. In addition to the SNR testing discussed above with respect to FIG. 18, another heuristic test is applied in the methods of the present invention to help prevent any instability in the notch filter calculations. A heuristic test depicted in FIG. 19 checks the computed notch filter weights for stability within a predetermined acceptable range. The newly computed filter weights will not be used for the next sample if they fall outside the acceptable range. In such a case, the previous values of the weights, computed from the previous sample values, will be used until a subsequent computation results in acceptable filter weights. Elements 1902-1908 are operable to determine the updated forgetting factor, the updated gain vector, the updated debiasing parameter, and the updated covariance matrix from the current sample values, Element 1910 is next operable to determine the updated notch filter weights given the previous weights (computed from the previous sample processing), the gain vector, and debiasing parameter values determined by operation of elements 1902-1908. As discussed above with respect to FIG. 18, when an error is sensed by testing the enhanced signal to noise ratio, the computations associated with the updated coefficients are reset to restart the convergence of the notch on the shifted fundamental frequency of the flow tubes. Element 1912 is operable to evaluate the stability of the newly computed weights against a predetermined range of acceptable values. If the newly computed weights are in the acceptable range, element 1914 operates to apply the new weights to the notch filters in preparation for the processing of the next decimated samples. If the newly computed weights are outside the acceptable range, the new weights are not applied to the filters, but rather, the previous weights (computed from the processing of the previous sample) are used again for the next decimated sample. A FIRST PREFERRED EMBODIMENT In a first exemplary preferred embodiment of the present invention, two adaptive notch filters are utilized, one for filtering discrete digitized samples from the left channel and a second for the right channel. The weight adaptation computations adjust the notch parameters for both adaptive notch filters by sampling the signals associated with the left channel processing. FIG. 3 decomposes elements of FIG. 2 to show additional detail regarding the flow of information between computational elements of FIG. 2. Computational elements 204 are the adaptive notch filters first depicted in FIG. 2. Left channel adaptive notch filter 204 receives decimated sensor output signal samples (x.sub.L) from path 254 (of FIG. 2). Weight coefficients (W) of the notch filter transfer function are received from weight adaptation element 210 over path 258. Debiasing parameter (.alpha.), which determines the shape of the notch, is also received from weight adaptation element 210 over path 258. Right channel adaptive notch filter 204 receives decimated sensor output signal samples (x.sub.R) from path 256 (of FIG. 2) but otherwise operates identically to left channel adaptive notch filter 204. Both the left and right channel adaptive notch filters receive the same adaptation parameters (W and .alpha.) over path 258 from weight adaptation element 210. Both the left and right channel adaptive notch filters 204 generate an enhanced signal represented by discrete sample values applied to their respective output paths 260 and 262 respectively. The enhanced signal, denoted e.sub.L and e.sub.R for the left and right channels respectively, represents the associated input signal samples filtered of all noise signals but for a narrow band of frequencies near the fundamental frequency of the vibrating flow tubes. Left channel adaptive notch filter 204 applies a signal representing the noise portion of the input signal samples (n.sub.L) and a value indicating the gradient vector of the input signal sample values (.PSI.) to its output path 260. These signal values (e.sub.L, n.sub.L, and .PSI.) are used by weight adaptation element 210 to determine the weight adaptation parameters for the next adjustment of the notch filter. Both left and right channel adaptive notch filters 204 compute the same functions, however, the noise and gradient values from the right channel adaptive notch filter are not used in the methods and apparatus of the present invention. In practice, the unused signals for the right channel adaptive notch filter 204 are not computed by the DSP software of the preferred embodiment. The functions computed by adaptive notch filters 204 are discussed in detail below. The enhanced signal values from the left and right channel adaptive notch filters 204 are received over paths 260 and 262, respectively, by phase computation elements 206. Phase computation elements 206 determine the phase of the sinusoidal signals represented by the enhanced discrete sample signals applied to their respective inputs on paths 260 and 262. The Fourier transform phase computation elements 206 utilize a Hanning window weighting method to sum 2N discrete weighted samples on each channel which represent eight cycles of the corresponding sinusoidal input signals. As discussed below, various computational elements in the present invention apply their respective computations to data received during half of the Hanning window period (samples 0 . . . N-1). The value SAMPNO indicative of the particular sample of the present half window cycle (sample 0 . . . N-1) is received as an input over path 272 to phase computation elements 206. The SAMPNO value is used as an index to a vector of weights applied to the enhanced sampled signal values for the first and seconds halves of the Hanning window. These weighting methods are employed by the phase computation elements 206 discussed below. Phase computation elements 206 apply a Goertzel filter Fourier transform to the filtered discrete sampled signal values to determine the phase of the sinusoidal signal on each channel of the system. The coefficients of the Goertzel filter (B--a complex number) are supplied to phase computation elements 206 by frequency computation element 212 over path 268. The Goertzel filter processes the samples in each Hanning window to generate a complex number representing the phase of the sampled sinusoidal sensor output signals. The complex number values generated by the phase computation elements 206 are applied to paths 264 and 266 for the left and right channels, respectively. .DELTA.t computation element 208 receives the complex numbers indicative of the phase of the sampled signals on paths 264 and 266 corresponding to the left and right channel signals, respectively. .DELTA.t computation element 208 receives a number (.OMEGA.) indicating the current the fundamental frequency of the vibrating flow tubes from frequency computation element 212 over path 268. To more fully utilize the data available from each channel, the phase, frequency, and .DELTA.t computations are performed every half window (half the Hanning window length as determined above). Two parallel phase computations are performed on the filtered discrete sampled input values on each channel. Each of the two parallel computations completes once for every full window of filtered discrete sample values. The parallel computations are offset from one another in time by the period corresponding to a number of samples equal to half the length of the Hanning window. Since the two computational elements are offset from one another by one half of the length of the Hanning window, one of the two parallel computations completes its computation every half window period on each channel. Therefore, every half window period of time, a new phase, frequency, and .DELTA.t computation is completed and utilized for mass flow rate measurements. Weight adaptation element 210 of FIG. 2 is shown decomposed into four sub-elements, namely SNR fault detection element 300, notch filter weight computation element 302, gain vector computation element 304, and debiasing parameter computation element 306. SNR fault detection element 300 receives the enhanced signal values (e.sub.L) and the noise component of the unfiltered sample values (n.sub.L), both generated by the left channel notch filter 204 and applied to path 260. SNR fault detection element 300 determines whether the energy ratio of the enhanced signal values (e.sub.L) to the noise component of the unfiltered sample values (n.sub.L) is below a threshold level. When the signal to noise ratio drops below a pre-determined lower limit, it typically indicates that the notch filter 204 is not converged on the fundamental frequency of the vibrating flow tubes. When the signal to noise ratio is found to be deficient, an SNR FAULT signal is generated and applied to the output of SNR fault detection element 300 on path 350 of FIG. 3. As discussed below, the SNR FAULT signal applied to path 350 is used by other computational elements within weight adaptation element 210 to restart the computations used to adapt the notch filter and to converge the notch on the fundamental frequency of the vibrating flow tubes. The precise computation and details of SNR fault detection element 300 are presented below with respect to FIG. 7. Notch filter weight computation element 302 receives the noise component of the unfiltered sample values (n.sub.L) generated by the left channel notch filter 204 and applied to path 260. Element 302 also receives the gain vector values (K--a two element vector) generated by gain vector computation element 304 and applied to path 352. In addition, element 302 receives the updated debiasing parameter (.alpha.') generated by debiasing parameter computation element 306 and applied to path 354. Notch filter weight computation element then computes the updated values of the notch filter weights (W) and applies them to path 258 for use by notch filters 204 and frequency calculation element 212. The precise computation and details of notch filter computation element 302 are presented below with respect to FIG. 6. Gain vector computation element 304 receives the gradient (.PSI.) generated by left channel notch filter 204 and applied to path 260. Element 304 also receives the SNR FAULT signal generated by SNR fault detection element 300 and applied to path 350. In addition, element 304 receives forgetting factor (.pi.) generated by debiasing parameter computation element 306 and applied to path 356. Gain vector computation element 304 then computes the updated values of the gain vector (K) and applies them to path 352 for use by notch filter weight computation element 302. The precise computation and details of gain vector computation element 304 are presented below with respect to FIG. 5. Debiasing parameter computation element 306 receives the SNR FAULT signal generated by SNR fault detection element 300 and applied to path 350. Debiasing parameter computation element 306 then computes the updated values of the debiasing parameter (.alpha.) and applies it to path 258 for use by notch filters 204. Debiasing parameter computation element 306 also computes an updated debiasing parameter (.alpha.') and applies it to path 354 for use by notch filter weight computation element 302. In addition, debiasing computation element 306 computes an updated forgetting factor (.pi.) and applies it to path 356 for use by gain vector computation element 304. The precise computation and details of debiasing parameter computation element 306 are presented below with respect to FIG. 8. Frequency calculation element 212 of FIG. 2 is shown decomposed into two sub-elements, namely Goertzel filter weights computation element 308 and half window coefficient pipeline 310. Goertzel filter weights computation element 308 accepts the notch filter weights determined by operation of weight adaptation element 210 and applied to path 258. Goertzel filter weights computation element 308 then determines the Goertzel filter weights (B') as a complex number and also determines the frequency (.OMEGA.') of the sinusoidal flow tube sensor output signal represented by the discrete sampled signal values and as contained in the weights of the notch filter. Both values so determined are computed at the end of each half window period as indicated by the haft window signal applied to path 274 by CLOCK 214 of FIG. 2. The Goertzel weights and frequency so determined are applied to path 358 for use by half window coefficient pipeline 310. The precise computation and details of Goertzel filter weights computation element 308 are presented below with respect to FIG. 9. Haft window coefficient pipeline 310 receives the Goertzel filter weights (B') and the frequency (.OMEGA.') both computed as above by Goertzel filter weights computation element 308. Half window coefficient pipeline 310 then adjusts the timing of the computed values (B' and .OMEGA.') to associate them with one of the two parallel computations for the overlapping half windows. The precise computation and details of half window coefficient pipeline 310 are presented below with respect to FIG. 10, As noted previously, the computations performed by the elements depicted in FIG. 3 (and other detailed figures discussed below) are preferably performed using floating point arithmetic to maintain accuracy over a broad scale of numeric precision. Floating point computation functions may be performed by hardware within the signal processor 2000 of FIG. 20 or may be emulated by processor 2000 using software library functions. Performance and cost factors will determine the choice between floating point hardware and software as appropriate for each application of the present invention. A FIRST EXEMPLARY EMBODIMENT- NOTCH FILTER FIG. 4 shows additional detail regarding the function and computations performed within adaptive notch filters 204 of FIG. 3. Both adaptive notch filters 204, one associated with the left channel and the other with the right channel, are identical in structure and the computations performed. The left channel adaptive notch filter 204 receives decimated discrete time sample sensor values as input from path 254 and applies its filtered output to path 260. The right channel adaptive notch filter 204 receives decimated discrete time sample sensor values as input from path 256 and applies its filtered output to path 262. Adaptive notch filter 204 also receives current weights (W a two element vector represented as W.sub.1, W.sub.2) and the debiasing parameter (.alpha.) from weight adaptation element 210 of FIG. 3 over path 258. Adaptive notch filter 204 generates the square of the debiasing parameter (.alpha..sup.2) by applying it from path 258 to both inputs of multiplication junction 446 the output of which is applied to path 488. A portion of the elements within adaptive notch filter 204 of FIG. 4, denoted by the dashed line box within the adaptive notch filter 204, are used to compute the gradient of the input signal samples (.PSI.a two element vector represented as .PSI..sub.1, .PSI..sub.2). The gradient value so computed is applied to path 260 in the adaptive notch filter 204 of the left channel. The gradient is used by the weight adaptation element 210 of FIG. 3 to compute updated notch filter weights for the next sample received on path 254. The elements in the dashed box of FIG. 4 used to compute the gradient are not used in the adaptive notch filter 204 of the right channel. The adaptive notch filter 204 of FIG. 4 determines the noise present in the discrete sample input values. Subtracting the noise signal values from the input sample values yields the enhanced filtered value for output on path 260. The adaptive notch filter 204 determines the enhanced signal value e by a second order filter polynomial and matrix arithmetic as follows (where variable(t) as used in the equations below indicates the value of variable corresponding to sample period "t"):
The pseudo-circuits of FIG. 4 describe these equations in the form of circuit and computational elements. Summing junction 400 sums the input signal value x on path 254 (256 for the right channel) and the intermediate computation value on path 452 (representing WAY as above) to generate y=x+WAY as above which is applied to path 450. The value of y on path 450 is applied as input to delay circuit 408 to delay it one sample clock period (CLK) then apply it to output path 460. The once delayed value of y on path 460 is input to delay circuit 436 to delay it a second sample clock period (CLK) then apply it to output path 468. The once delayed value of y on path 460 and the twice delayed value of y on path 468 represent the vector Y as above. The debiasing diagonal matrix A is comprised of the debiasing parameter and its square (.alpha. and .alpha..sup.2) on paths 258 and 488, respectively. The vector Y on paths 460 and 468 is multiplied by the debiasing diagonal matrix A applied to paths 258 and 488 through multiplication junctions 406 and 434, respectively, to produce AY on paths 458 and 470, respectively. This product is in turn multiplied by the weights vector W applied to path 258 through multiplication junctions 404 and 432, respectively, to produce intermediate computational values on paths 456 and 454, respectively. The two intermediate values on paths 456 and 454, respectively, are applied to summing junction 402 to produce the scalar value WAY on path 452 as described above. The vector Y on paths 460 and 468 is also multiplied by the weights vector W on path 258 through multiplication junctions 414 and 438, respectively, to produce intermediate values on paths 464 and 466. The two intermediate values on paths 464 and 466 are summed through summing junction 416 to produce the value WY on path 462. Summing junction 412 subtracts the value WY on path 462 from the value y on path 450 to produce the noise value n=y-WY on path 470. In the adaptive notch filter 204 of the left channel, this value representing the noise portion n of the input sample values x is applied to path 260 for use in the weight adaptation element 210 of FIG. 3. Summing junction 410 subtracts the noise value n on path 470 from the input sample value x on path 254 (256 for the right channel) to produce the enhanced signal value e=x-n on path 260 (262 for the right channel). The enhanced signal value is used in subsequent phase computation elements 206 and in weight adaptation element 210 as discussed below. In addition to the noise value, n, and the enhanced signal value, e, the adaptive notch filter 204 computes the gradient vector .PSI. as .PSI..sub.1, and .PSI..sub.2 on path 260. The adaptive notch filter 204 determines the gradient vector .PSI. a second order filter polynomial and matrix arithmetic as follows:
Summing junction 418 adds the noise value n on its input path 470 to the intermediate computation value WAF on its input path 474 to produce f=n+WAF on path 472. The value f on path 472 is applied as input to delay circuit 420 to delay it one sample clock period (CLK) then apply it to output path 476. The once delayed value of f on path 476 is input to delay circuit 430 to delay it a second sample clock period (CLK) then apply it to output path 484. The once delayed value of f on path 476 and the twice delayed value of f on path 484 represent the vector F as above. The vector F on paths 476 and 484 is multiplied by the debiasing diagonal matrix A applied to paths 258 and 488 through multiplication junctions 426 and 442, respectively, to produce AF on paths 478 and 486 respectively. This product is in turn multiplied by the weights vector W applied to path 258 through multiplication junctions 424 and 440, respectively, to produce intermediate computational values on paths 480 and 482, respectively. The two intermediate values on paths 480 and 482, respectively, are applied to summing junction 422 to produce the scalar value WAF on path 474 as described above. The intermediate product AF on paths 478 and 486 is subtracted from the Y vector on paths 460 and 468 through summing junctions 428 and 444 to produce the gradient vector .PSI. (.PSI..sub.1, .PSI..sub.2)=Y-AF and apply it path 260. The gradient vector on path 260 is used by weight adaptation element 210 of FIG. 3 to compute the updated notch filter weights. Both the left and right channel adaptive notch filters 204 operate as described above. The computation of the gradient vector .PSI. and the noise value n on path 260 is unnecessary in the right channel and so may be skipped as a computational step in the right channel. The weight adaptation element 210 utilizes only the values from the left channel applied to path 260 to adjust the weights for both adaptive notch filters 204. Only the enhanced signal value e is used from the right channel and is applied to path 262 for use by the phase computation elements 206. A FIRST EXEMPLARY EMBODIMENT- WEIGHT ADAPTATION The weight adaptation element 210 of FIG. 3 receives the enhanced signal value e.sub.L, the noise portion of the unfiltered input signal n.sub.L, and the gradient .PSI. all generated by the adaptive notch filter 204 of the left channel and applied to path 260. Weight adaptation element 210 then determines the weights vector W and the debiasing parameter .alpha. and applies them to path 258 to adjust notch filters of both channels for the next discrete sampled value to be processed in adaptive notch filter 204. To simplify this description of the weight adaptation functions, weight adaptation element 210 is decomposed into four sub-elements each performing portions of the total computation, namely SNR fault detection element 300, notch filter weight computation element 302, gain vector computation element 304, and debiasing parameter computation element 306. SNR fault detection element 300, depicted in additional detail in FIG. 7, receives the enhanced signal values (e.sub.L) and the noise component of the unfiltered sample values (n.sub.L), both generated by the left channel notch filter 204 and applied to path 260. SNR fault detection element 300 determines whether the energy ratio of the enhanced signal values (e.sub.L) to the noise component of the unfiltered sample values (n.sub.L) is below a threshold level as discussed above. SNR fault detection element 300 is depicted in additional detail in FIG. 7. The SNR fault detection element 300 of FIG. 7 determines the signal to noise ratio by summing the noise energy and summing the noise cancelled energy, then comparing the ratio of the two values against a pre-determined threshold values. SNR fault detection element receives the enhanced signal value e.sub.L and the noise signal n.sub.L from the left channel over path 260. The noise signal value is applied to both inputs of multiplication junction 700 to produce the square of the noise signal n.sup.2 and apply it to path 750. The n.sup.2 value on path 750 is applied to one input of a 2:1 mux 704 and to one input of summing junction 706. The output of mux 704 is applied to the input of register 712 via path 758. Register 712 stores the value on its input when clocked by the CLK sample clock. The current value in register 712 is applied to its output through path 764 to the other input of summing junction 706. The sum output of summing junction 706 is applied over path 754 to the other input of mux 704. At the start of each half window period, as signalled on path 274, mux 704 selects its input connected to the n.sup.2 value on path 750 to restart the summing of the noise energy for a new half window period. For all other samples in the half window period, mux 704 selects its input connected to path 754 to accumulate the noise energy. The accumulated noise energy is accumulated in register 712 for each sample in the half window period and the current accumulated sum is applied to the output of register 712 on path 764. The accumulated sum is restarted on each new half window period. The noise-cancelled signal energy is accumulated in like fashion by squaring and accumulating the enhanced signal value received on path 260. The noise-cancelled energy is accumulated by operation of multiplication junction 702, summing junction 710, mux 708, and register 714, over paths 752, 756, 760, and 762 in a similar manner to that described above for accumulation of noise energy. The noise-cancelled energy accumulated through each half window period of sampled values is applied to the output of register 714 to path 762. Computational element 716 receives the accumulated noise energy over path 764 and the accumulated noise-cancelled energy over path 762 and compares the values to pre-determined threshold values. The ratio of the accumulated noise-cancelled value and the accumulated noise value is the signal to noise ratio. If the ratio drops below a pre-determined threshold, then a signal to noise ratio fault condition is detected and a signal so indicating is applied to the output of computational element 716. Fault timing element 718 receives the fault condition signal on path 766 generated by computational element 716 and receives the half window signal on path 274. When a fault condition is sensed on input path 766, fault timing element 718 applies a pulse signal to SNR FAULT on path 350. The SNR fault signal on path 350 is sensed by other sub-elements within weight adaptation element 210 to force a reset of various notch parameter computations. Following application of a signal to SNR FAULT, fault timing element 718 enforces a grace period during which no further signals are applied to the SNR FAULT signal on path 350. The grace period is intended to allow the notch filter parameters a period of time to re-converge on the fundamental frequency of the vibrating flow tubes. The fault timing element 718 also enforces a grace period during |