Method and system for CELP speech coding and codebook for use therewith

• US 5,371,853 A
• Filed: 10/28/1991
• Issued: 12/06/1994
• Est. Priority Date: 10/28/1991
• Status: Expired due to Fees

• Current Assignee: University of Maryland
• Original Assignee: University of Maryland
• Inventors: Baras, John, Kao, Yuhung
• Primary Examiner(s): MacDonald, Allen R.
• Assistant Examiner(s): KNEPPER, DAVID D
• Application Number:
  US07/783,127
• Time in Patent Office: 1,135 Days
• Field of Search: 381/36, 381/38, 381/31, 395/2.32
• US Class Current: 704/200.1
• 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 2019/0004 Design or structure of the codebook
• CPC Class Codes: G10L 2019/0008 Algebraic codebooks
• CPC Class Codes: G10L 2019/0011 Long term prediction filters, i.e. pitch estimation

Abstract

Apparatus and method for encoding speech using a codebook excited linear predictive (CELP) speech processor and an algebraic codebook for use therewith. The CELP speech processor receives a digital speech input representative of human speech and performs linear predictive code analysis and perceptual weighting filtering to produce a short term speech information and a long term speech information. The CELP speech processor utilizes an organized, non-overlapping, algebraic codebook containing a predetermined number of vectors, uniformly distributed over a multi-dimensional sphere to generate a remaining speech residual. The short term speech information, long term speech information and remaining speech residual are combinable to form a quality reproduction of the digital speech input.

Claims

1. A codebook excited linear predictive (CELP) speech processor comprising:

means for supplying a digital speech input representative of human speech;

means for performing linear predictive code analysis and perceptual weight filtering on said digital speech input to obtain short term speech information;

means for performing linear predictive code analysis and perceptual weight filtering on said digital speech input to obtain long term speech information;

a deterministic non-overlapping codebook of a first predetermined number of vectors which are uniformly distributed over a multi-dimensional sphere, each of the first predetermined number of vectors being partitioned into a second predetermined number of sub-vectors, a substantial number of elements of each of the second predetermined number of sub-vectors being defined as zero, and a remaining even number of elements of each of the second predetermined number of sub-vectors defined as +1 or -1, wherein four elements with an index=5N (where N is an integer from 0 to

     3) are non-zero for each of the second predetermined number of subvectors and the four non-zero elements of each of the second predetermined number of sub-vectors are all -1, all +1, or two are -1 and two are +1; and

means for generating a remaining speech residual of the digital speech input from the deterministic codebook;

the short term speech information, the long term speech information and the remaining speech residual being combinable to form a quality reproduction of the digital speech input to reproduce the human speech represented by said digital speech input.

2. The codebook excited linear predictive (CELP) speech processor of claim 1, said means for generating a remaining speech residual including,

means for calculating a plurality of inner products for a speech residual vector, representative of the remaining speech residual, with respect to each of the first predetermined number of vectors.

3. The codebook excited linear predictive (CELP) speech processor of claim 2, said means for calculating a plurality of inner products including,

means for selecting the remaining even number of elements of each of the second predetermined number of subvectors defined as +1 or -1,

means for calculating a plurality of sums for each of the second predetermined number of subvectors, based on the selected remaining even numbers of elements, for each of the first predetermined number of vectors,

means for selecting all possible combinations of the plurality of sums for each of the second predetermined number of subvectors,

means for summing all possible combinations of the plurality of sums for each of the second predetermined number of subvectors, to obtain the plurality of inner products,

means for perceptual weighting each of the first predetermined number of vectors by convolving each of the first predetermined number of vectors with an impulse response, utilizing a FIR filter, and

means for detecting an energy level for each of the first predetermined number of vectors.

4. The codebook excited linear predictive (CELP) speech processor of claim 1, wherein said CELP speech processor is used to transmit and receive a digital speech input, representative of human speech, at data rates from 2.4 Kbps to 16 Kbps.

5. The codebook excited linear predictive (CELP) speech processor of claim 4, wherein said CELP speech processor is used to transmit and receive a digital speech input, representative of human speech, at a data rate of 4.8 kbps.

6. The codebook excited linear predictive (CELP) speech processor of claim 1, wherein the multi-dimensional sphere is 60-dimensional.

7. The codebook excited linear predictive (CELP) speech processor of claim 1, wherein the first predetermined number of vectors, uniformly distributed over the 60-dimensional sphere is equal to 512.

8. The codebook excited linear predictive (CELP) speech processor of claim 7, wherein the second predetermined number of subvectors is equal to 1,536, and wherein each subvector contains 20 elements.

9. The codebook excited linear predictive (CELP) speech processor of claim 8, wherein a value of each of the elements of the 1,536 subvectors is -1, 0, or 1.

10. The codebook excited linear predictive (CELP) speech processor of claim 9, wherein 80% of the elements of each of the 1,536 subvectors is equal to zero.

11. The codebook excited linear predictive (CELP) speech processor of claim 10, wherein an even number of elements of each of the 1,536 subvectors are non-zero.

12. A method of encoding speech data including the steps of providing a digital speech input, performing linear predictive code analysis and perceptual weight filtering on the digital speech input to produce a short and long term speech information and generating a deterministic non-overlapping codebook of a first predetermined number of vectors which are uniformly distributed over a multi-dimensional sphere comprising the steps of:

a) partitioning each of the first predetermined number of vectors into a second predetermined number of sub-vectors;

b) setting a substantial number of elements of each of the second predetermined number of sub-vectors to zero;

c) setting a remaining even number of elements of each of the second number of sub-vectors to 1 or -1, wherein four elements with an index of SN (where N is an integer from 0 to

     3) are non-zero for each of the second number of sub-vectors and the four non-zero elements of each sub-vector are all -1, all +1, or two are -1 and two are +1; and

d) generating a remaining speech residual of the digital speech input from the deterministic codebook such that the short and long term speech information and the remaining speech residual are combinable to form a quality reproduction of the digital speech input.

13. The method of encoding speech data of claim 12, said generating step including,

calculating a plurality of inner products for a speech residual vector, representative of the remaining speech residual, with respect to each of the first predetermined number of vectors.

14. The method of encoding speech data of claim 13, said calculating step including,

selecting the remaining even number of elements of each of the second predetermined number of subvectors defined as +1 or -1,

calculating a plurality of sums for each of the second predetermined number of subvectors, based on the selected remaining even number of elements, for each of the first predetermined number of vectors,

selecting all possible combinations of the plurality of sums for each of the second predetermined number of subvectors,

summing all possible combinations of the plurality of sums for each of the second predetermined number of subvectors, to obtain the plurality of inner products,

perceptual weighing each of the first predetermined number of vectors by convolving each of the first predetermined number of vectors with an impulse response, utilizing a FIR filter, and

detecting an energy level for each of the first predetermined number of vectors.

15. The method of claim 12, wherein a data rate of the digital speech input and the quality reproduction of the digital speech input is from 2.4 kbps to 16 kpbs.

16. The method of claim 15, wherein a data rate of the digital speech input and the quality reproduction of the digital speech input is 4.8 kbps.

17. The method of claim 12, wherein the multi-dimensional sphere is 60-dimensional.

18. The method of claim 12, wherein the first predetermined number of vectors, uniformly distributed over the 60-dimensional sphere is equal to 512.

19. The method of claim 18, wherein the second predetermined number of subvectors is equal to 1,536, and wherein each subvector contains 20 elements.

20. The method of claim 19, wherein the value of each of the elements of the 1,536 subvectors is -1, 0, or 1.

21. The method of claim 20, wherein 80% of the elements of each of the 1,536 subvectors is equal to zero.

22. The method of claim 21, wherein an even number of elements of each subvector are non-zero.