Method and apparatus for low-delay CELP speech coding and decoding

• US 5,233,660 A
• Filed: 09/10/1991
• Issued: 08/03/1993
• Est. Priority Date: 09/10/1991
• Status: Expired due to Term

• Current Assignee: American Telephone & Telegraph Company (AT&T, Inc.)
• Original Assignee: AT&T, Inc.
• Inventors: Chen, Juin-Hwey
• Primary Examiner(s): KEMENY, EMANUEL
• Application Number:
  US07/757,168
• Time in Patent Office: 693 Days
• Field of Search: 381/29-38, 395/2, 375/27
• US Class Current: 704/208
• CPC Class Codes: G10L 19/08 Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters
• CPC Class Codes: G10L 19/12 the excitation function being a code excitation, e.g. in code excited linear prediction [CELP] vocoders
• CPC Class Codes: G10L 19/18 Vocoders using multiple modes
• CPC Class Codes: G10L 19/26 Pre-filtering or post-filtering
• CPC Class Codes: G10L 2019/0002 Codebook adaptations
• CPC Class Codes: G10L 2019/0003 Backward prediction of gain
• CPC Class Codes: G10L 2019/0011 Long term prediction filters, i.e. pitch estimation
• CPC Class Codes: G10L 2019/0013 Codebook search algorithms
• CPC Class Codes: G10L 2025/786 Adaptive threshold
• CPC Class Codes: G10L 2025/906 Pitch tracking
• CPC Class Codes: G10L 25/06 the extracted parameters being correlation coefficients
• CPC Class Codes: G10L 25/93 Discriminating between voiced and unvoiced parts of speech signals G10L25/90 takes precedence

Abstract

A low-bitrate (typically 8 kbit/s or less), low-delay digital coder and decoder based on Code Excited Linear Prediction for speech and similar signals features backward adaptive adjustment for codebook gain and short-term synthesis filter parameters and forward adaptive adjustment of long-term (pitch) synthesis filter parameters. A highly efficient, low delay pitch parameter derivation and quantization permits overall delay which is a fraction of prior coding delays for equivalent speech quality at low bitrates.

Claims

1. In a method of coding F-millisecond frames of samples of an input signal sampled at a rate of R kilobits per second with a coding delay of D milliseconds comprising the steps of

for each of a plurality of codebook vectors having respective index signals

adjusting said vectors by a gain factor to generate a

gain-adjusted vector,

applying said gain-adjusted vector to the cascade of

a long-term filter reflecting long-term characteristics of said input signals and

a short-term filter reflecting short-term characteristics of said input signals,

thereby to generate a synthesized candidate signal comparing each of said candidate signals with said frame of sampled input signals to determine the candidate signal best approximating said frame of sampled input signals, making available the index corresponding to the candidate signal best approximating said frame of sampled input signals for subsequent decoding of said frame,

deriving filter parameters for said long term filter,

making available said filter parameters for subsequent decoding of said frame,

The Improvement Comprising the further step of

deriving filter parameters for said short-term filter by backward adaptation.

2. The method of claim 1, wherein said short-term filter is a filter having a number NS<

20 filter taps, and said step of deriving filter parameters for said short-term filter comprises deriving coefficient values for each of said NS taps.

3. The method of claim 1, wherein F is less than or equal to 5.

4. The method of claim 1, wherein D is less than or equal to 10.

5. The method of claim 4, wherein R is less than 16.

6. The method of claim 2, wherein said gain factor is adjusted by backward adaption.

7. The method of claim 1, wherein said step of comparing comprises

forming for each candidate signal a difference signal representing the difference between said input frame and said candidate signal,

frequency weighting said difference signals to form weighted difference signals that emphasize frequencies of greater perceptual significance, and

determining the minimum value for said weighted difference signals.

8. The method of claim 7, wherein said frequency weighting is accomplished by filtering said difference signals in a filter, the coefficients of which are determined by an analysis of the input frame signals.

9. The method of claim 8, wherein said analysis of the input frame signals comprises an LPC analysis of the unquantized input frame signals.

10. The method of claim 2, wherein NS=10.

11. The method of claim 1 wherein said step of deriving filter parameters for said long-term filter comprises deriving a pitch period parameter and NL>

1 filter tap coefficient parameters.

12. The method of claim 11, wherein NL=3.

13. The method of claim 1 further comprising the steps of

determining whether or not said frame of sampled input signals is part of a sequence of voiced information, and

making available said filter parameters for said long-term filter for decoding when said frame of sampled input signals is part of a sequence of voiced information.

14. The method of claim 13, wherein said step of determining comprises

making a preliminary voiced/non-voiced decision for each frame, and

determining that the present frame is not part of the sequence of voiced speech frames if the preliminary decision for the present frame and for each of a predetermined number, K, of immediately preceding frames is non-voiced.

15. The method of claim 14, wherein said step of making a preliminary voiced/non-voiced decision comprises

establishing a threshold value for samples in the input frame,

adjusting said threshold for each succeeding sample in the input frame by

multiplying the existing threshold by a predetermined factor T<

1 whenever the value for the present sample is less than or equal to the existing threshold, and

setting the threshold to the value of the present sample when it exceeds the existing threshold,

forming for each input frame a reference value based on the threshold values for the samples in the frame, and

making a decision that the current frame is voiced whenever the values for samples in the current frame satisfy a first predetermined condition relative to said reference value, and

making a preliminary decision that the current frame is non-voiced whenever the values for samples in the current frame satisfy a second predetermined condition relative to said reference value.

16. The method of claim 15, wherein

said step of forming a reference value comprises forming an average of the threshold function for the samples of the current frame,

said first predetermined condition comprises having the peak magnitude for samples in the current frame exceeding half of said reference value,

said second predetermined condition comprises having the peak magnitude for samples in the current frame failing to exceed 2% of said reference value, and

said method further comprises

determining the optimal tap value for a one-tap predictor based on the current input frame, and

whenever said first and second predetermined conditions are not satisfied, determining that said present frame is voiced if said one-tape predictor tap value is greater than a predetermined value.

17. The method of claim 15, wherein

said step of forming a reference value comprises forming an average of the threshold function for the samples of the current frame,

said first predetermined condition comprises having the peak magnitude for samples in the current frame exceeding half of said reference value,

said second predetermined condition comprises having the peak magnitude for samples in the current frame failing to exceed 2% of said reference value, and

said method further comprises

determining the normalized first-order autocorrelation coefficient of the samples of the present frame, and

whenever said first and second conditions are not satisfied, determining that said current frame is voiced whenever said autocorrelation coefficient is greater than a predetermined value.

18. method of claim 15, wherein

said step of forming a reference value comprises forming an average of the threshold function for the samples of the current frame,

said first predetermined condition comprises having the peak magnitude for samples in the current frame exceeding half of said reference value,

said second predetermined condition comprises having the peak magnitude for samples in the current frame failing to exceed 2% of said reference value, and

said method further comprises

determining the zero-crossing rate for the samples of the present frame, and

whenever said first and second conditions are not satisfied, determining that said current frame is voiced whenever said zero-crossing rate is greater than a predetermined value.

19. The method of claim 14, wherein K=3.

20. The method of claim 11, wherein said step of deriving said pitch period for said long term filter comprises

performing an L-order LPC analysis of the signals in the input frame,

performing an inverse LPC filtering of said input frame signals based on filter coefficients derived from said L-order analysis to determine a prediction residual signal, and

extracting said pitch period by correlation peak picking of a function of said prediction residual signal.

21. The method of claim 20, wherein said function of said prediction residual signal is a low-pass filtered, time-decimated function of said prediction residual signal.

22. The method of claim 20, wherein said correlation peak picking is performed for time lags extending over a range of possible pitch period durations, and said extraction comprises selecting the time lag yielding the largest correlation.

23. The method of claim 21, wherein said correlation peak picking is performed for time lags extending over a range of possible pitch period durations, and said extraction comprises

selecting the time lag yielding the largest correlation, and

adjusting said selected time lag to account for said time-decimation to yield a pitch period value p0.

24. The method of claim 23 further comprising eliminating a false multiple of the true pitch period from said adjusted time lag by

establishing the pitch period determined for the previous period as a reference value, and

selecting a pitch period value p1 for the present frame which is indicated by a peak in said peak picking which peak is within a preselected range of said reference value,

the reference value for the first frame in a sequence of frames having a significant pitch component being selected as the peak of said correlation function without reference to a preceding pitch period value.

25. The method of claim 24 further comprising resolving possible conflicts between a value for a pitch period p1 within said preselected range and a pitch period p0 outside said range comprising

determining the optimal tap weight for a single-tap predictor based on said input frame with a pitch period p0 and normalizing it to a range between 0 and 1, thereby forming a value WON,

determining the optimal tap weight for a single-tap predictor based on said input frame with a pitch period p1 and normalizing it to a range between 0 and 1, thereby forming a value W1N,

when W1N is greater than or equal to a predetermined fraction of W0N, selecting then p1 is selected as the correct pitch estimate, otherwise p0 is selected as the correct pitch estimate.

26. The method of claim 25, wherein said predetermined fraction is substantially equal to 0.4.

27. The method of claim 11, wherein said step of making available filter parameters for said long-term filter comprises

generating a first estimate of the pitch period from the current frame of input samples,

generating a rounded representation, r, of said first estimate of the pitch period,

generating a second estimate of the pitch period by the open-loop steps of

performing an L-order LPC analysis of the signals in the input frame,

performing an inverse LPC filtering of said input frame signals based on filter coefficients derived from said L-order analysis to determine a prediction residual signal, and

extracting said second pitch period estimate by correlation peak picking of a function of said prediction residual signal,

forming a difference signal representative of the difference between said second pitch period estimate and said rounded representation of said first estimate of the pitch period,

when said difference signal has a magnitude greater than a preselected value,

quantizing said difference signal into one of a plurality, q, of predetermined values, and

forming a quantized value, p, for said pitch period in accordance with p=r+q,

when said difference signal has a magnitude less than or equal to said preselected value, optimizing the quantization of said pitch period value in a closed loop quantization method.

28. The method of claim 27 wherein said generating a first estimate of said pitch period comprises forming an open-loop pitch prediction based on said input frame.

29. The method of claim 28 wherein said forming an open-loop pitch prediction comprises

determining whether said input frame comprises samples representative of voiced information, and

when said input frame does not comprise input information that is voiced, setting said first estimate of said pitch period to a predetermined value.

30. The method of claim 29, wherein said setting said first estimate of said pitch period to a predetermined value comprises setting such value to a value between approximately 10% and 50% from the lower extremity of the expected range of the pitch periods.

31. The method of claim 11 wherein said deriving a pitch period comprises

forming a first estimate of said pitch period using a prediction based on said input frame,

forming a second estimate based on a prediction of the pitch period for the immediately preceding frame,

forming a difference signal representing the difference between said first and second estimates,

if said difference signal is greater than a predetermined value, quantizing said difference value to one of a fixed plurality of values to form a quantized difference signal, and

deriving said pitch period from the sum of said second estimate and said quantized difference signal.

32. The method of claim 31, wherein said forming a second estimate comprises

delaying the value of the predicted value for the immediately preceding frame,

subtracting a fixed pitch bias value from said delayed value to yield a bias-adjusted value,

adjusting the magnitude of said bias-adjusted value to from a magnitude-adjusted value,

adding said fixed pitch bias value to said magnitude-adjusted value to form a predicted pitch period signal.

33. The method of claim 32, comprising the further step of rounding said predicted pitch period signal to form a rounded predicted pitch value.

34. The method of claim 13, wherein said deriving of filter parameters for said long-term filter comprises the steps of

setting said filter parameters to fixed predetermined values not dependent on the particular values for the input signals when said frame of input signals does not represent voiced information.

35. The method of claim 34, wherein said deriving of filter parameters for said long-term filter comprises setting said pitch period parameter to a value between about 10 % and 50% from the lower extremity of the expected range of values for the pitch period for input frames containing voiced information.

36. The method of claim 35, further comprising setting filter tap coefficients equal to a zero value when said frame of input signals does not represent voiced signals.