Method and apparatus for interleaving line spectral information quantization methods in a speech coder

• US 6,393,394 B1
• Filed: 07/19/1999
• Issued: 05/21/2002
• Est. Priority Date: 07/19/1999
• Status: Expired due to Term

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Manjunath, Sharath, Ananthapadmanabhan, Arasanipalai K.
• Primary Examiner(s): Dorvil, Richemond
• Assistant Examiner(s): Nolan, Daniel A.
• Application Number:
  US09/356,755
• Time in Patent Office: 1,037 Days
• Field of Search: 704/223, 704/214, 704/222, 704/219, 704/220, 704/208, 704/230, 704/200, 704/201, 704/270
• US Class Current: 704/230
• CPC Class Codes: G10L 19/07 Line spectrum pair [LSP] vocoders
• CPC Class Codes: G10L 2019/0005 Multi-stage vector quantisation
• CPC Class Codes: G10L 25/12 the extracted parameters being prediction coefficients

Abstract

A method and apparatus for interleaving line spectral information quantization methods in a speech coder includes quantizing line spectral information with two vector quantization techniques, the first technique being a non-moving-average prediction-based technique, and the second technique being a moving-average prediction-based technique. A line spectral information vector is vector quantized with the first technique. Equivalent moving average codevectors for the first technique are computed. A memory of a moving average codebook of codevectors is updated with the equivalent moving average codevectors for a predefined number of frames that were previously processed by the speech coder. A target quantization vector for the second technique is calculated based on the updated moving average codebook memory. The target quantization vector is vector quantized with the second technique to generate a quantized target codevector. The memory of the moving average codebook is updated with the quantized target codevector. Quantized line spectral information vectors are derived from the quantized target codevector.

Claims

1. A speech coder, comprising:

a linear predictive filter configured to analyze a frame and generate a line spectral information codevector based thereon; and

a quantizer coupled to the linear predictive filter and configured to vector quantize the line spectral information vector with a first vector quantization technique that uses a non-moving-average prediction-based vector quantization scheme,

wherein the quantizer is further configured to compute equivalent moving average codevectors for the first technique;

update a moving average codebook of codevectors for a predefined number of frames that were previously processed by the speech coder with the equivalent moving average codevectors;

compute a target quantization vector for the second technique based on the updated moving average codebook memory;

vector quantize the target quantization vector with a second vector quantization technique to generate a quantized target codevector;

the second vector quantization technique using a moving-average prediction-based scheme;

update the memory of the moving average codebook with the quantized target codevector;

and compute quantized line spectral information vectors from the quantized target codevector.

2. The speech coder of claim 1, wherein the frame is a frame of speech.

3. The speech coder of claim 1, wherein the frame is a frame of linear prediction residue.

4. The speech coder of claim 1, wherein the target quantization vector is computed in accordance with the following equation:

$U_M\equiv <br><br>\left\{U_M^n=\frac{\left(L_M^n-{\mathrm{\alpha <br><br>}}_1^n<br><br>{\hat{U}}_{M-1}^n-{\mathrm{\alpha <br><br>}}_2^n<br><br>{\hat{U}}_{M-2}^n-\mathrm{\dots <br><br>}-{\mathrm{\alpha <br><br>}}_P^n<br><br>{\hat{U}}_{M-P}^n\right)}{{\mathrm{\alpha <br><br>}}_0^n};<br><br>n=0,1,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>},N-1\right\},$

wherein {Û

<sub>M−

1</sub>ⁿ, Û

<sub>M−

2</sub>ⁿ, . . . , Û

<sub>M−

P</sub>ⁿ;

n=0,1, . . . , N−

1} are codebook entries corresponding to line spectral information parameters of the predefined number of frames processed immediately prior to the frame, and {α

₁ⁿ,α

₂ⁿ, . . . ,α

_Pⁿ;

n=0,1, . . . , N−

1} are respective parameter weights such that {α

₀ⁿ+α

₁ⁿ+, . . . ,+α

_Pⁿ=1;

n=0,1, . . . , N−

1}.

5. The speech coder of claim 1, wherein the quantized line spectral information vectors are computed in accordance with the following equation:

6. The speech coder of claim 1, wherein the equivalent moving average codevectors are computed in accordance with the following equation:

${\tilde{\hat{U}}}_{M-K}\equiv <br><br>\left\{{\tilde{\hat{U}}}_{M-K}^n=\frac{\left({\hat{L}}_{M-R}^n-{\mathrm{\beta <br><br>}}_1^n<br><br>{\hat{U}}_{M-K-1}^n-{\mathrm{\beta <br><br>}}_2^n<br><br>{\hat{U}}_{M-K-2}^n-\mathrm{\dots <br><br>}-{\mathrm{\beta <br><br>}}_R^n<br><br>{\hat{U}}_{M-K-P}^n\right)}{{\mathrm{\beta <br><br>}}_0^n};<br><br>n=0,1,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>},N-1\right\},$

wherein {β

₁ⁿ,β

₂ⁿ, . . . ,β

_Pⁿ;

n=0,1, . . . , N−

1} are respective equivalent moving average codevector element weights such that {β

₀ⁿ+β

₁ⁿ+, . . . ,+β

_Pⁿ=1;

n=0,1, . . . , N−

1}, and wherein an initial condition of {{circumflex over ({tilde over (U)})}<sub>−

1</sub>, {circumflex over ({tilde over (U)})}<sub>−

2</sub>, . . . ,{circumflex over ({tilde over (U)})}<sub>−

P</sub>} is established.

7. The speech coder of claim 1, wherein the speech coder resides in a subscriber unit of a wireless communication system.

8. A method of vector quantizing a line spectral information vector of a frame, using first and second quantization vector quantization techniques, the first technique using a non-moving-average prediction-based vector quantization scheme, the second technique using a moving-average prediction-based vector quantization scheme, the method comprising the steps of:

vector quantizing the line spectral information vector with the first vector quantization technique;

computing equivalent moving average codevectors for the first technique;

updating with the equivalent moving average codevectors a memory of a moving average codebook of codevectors for a predefined number of previously processed frames;

calculating a target quantization vector for the second technique based on the updated moving average codebook memory;

vector quantizing the target quantization vector with the second vector quantization technique to generate a quantized target codevector;

updating the memory of the moving average codebook with the quantized target codevector; and

deriving quantized line spectral information vectors from the quantized target codevector.

9. The method of claim 8, wherein the frame is a frame of speech.

10. The method of claim 8, wherein the frame is a frame of linear prediction residue.

11. The method of claim 8, wherein the calculating step comprises calculating the target quantization in accordance with the following equation:

$U_M\equiv <br><br>\left\{U_M^n=\frac{\left(L_M^n-{\mathrm{\alpha <br><br>}}_1^n<br><br>{\hat{U}}_{M-1}^n-{\mathrm{\alpha <br><br>}}_2^n<br><br>{\hat{U}}_{M-2}^n-\mathrm{\dots <br><br>}-{\mathrm{\alpha <br><br>}}_P^n<br><br>{\hat{U}}_{M-P}^n\right)}{{\mathrm{\alpha <br><br>}}_0^n};<br><br>n=0,1,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>},N-1\right\},$

wherein {Û

<sub>M−

1</sub>ⁿ,Û

<sub>M−

2</sub>ⁿ, . . . ,Û

<sub>M−

P</sub>ⁿ;

n=0,1, . . . , N−

1} are codebook entries corresponding to line spectral information parameters of the predefined number of frames processed immediately prior to the frame, and {α

₁ⁿ,α

₂ⁿ, . . . ,α

_Pⁿ;

n=0,1, . . . , N−

1}, are respective parameter weights such that {α

₀ⁿ+α

₁ⁿ+, . . . , +α

_Pⁿ=1;

n=0,1, . . . , N−

1}.

12. The method of claim 8, wherein the deriving step comprises deriving the quantized line spectral information vectors in accordance with the following equation:

13. The method of claim 8, wherein the computing step comprises computing the equivalent moving average codevectors in accordance with the following equation:

${\tilde{\hat{U}}}_{M-K}\equiv <br><br>\left\{{\tilde{\hat{U}}}_{M-K}^n=\frac{\left({\hat{L}}_{M-R}^n-{\mathrm{\beta <br><br>}}_1^n<br><br>{\hat{U}}_{M-K-1}^n-{\mathrm{\beta <br><br>}}_2^n<br><br>{\hat{U}}_{M-K-2}^n-\mathrm{\dots <br><br>}-{\mathrm{\beta <br><br>}}_R^n<br><br>{\hat{U}}_{M-K-P}^n\right)}{{\mathrm{\beta <br><br>}}_0^n};<br><br>n=0,1,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>},N-1\right\},$

wherein {β

₁ⁿ,β

₂ⁿ, . . . ,β

_Pⁿ;

n=0,1, . . . , N−

1} are respective equivalent moving average codevector element weights such that {β

₀ⁿ+β

₁ⁿ+, . . . ,+β

_Pⁿ=1;

n=0,1, . . . , N−

1}, and wherein an initial condition of {{circumflex over ({tilde over (U)})}<sub>−

1</sub>,{circumflex over ({tilde over (U)})}<sub>−

2</sub>, . . . ,{circumflex over ({tilde over (U)})}<sub>−

P</sub>} is established.

14. A speech coder, comprising:

means for vector quantizing a line spectral information vector of a frame with a first vector quantization technique that uses a non-moving average prediction-based vector quantization scheme;

means for computing equivalent moving average codevectors for the first technique;

means for updating with the equivalent moving average codevectors a memory of a moving average codebook of codevectors for a predefined number of frames that were previously processed by the speech coder;

means for calculating a target quantization vector for the second technique based on the updated moving average codebook memory;

means for vector quantizing the target quantization vector with the second vector quantization technique to generate a quantized target codevector;

means for updating the memory of the moving average codebook with the quantized target codevector; and

means for deriving quantized line spectral information vectors from the quantized target codevector.

15. The speech coder of claim 14, wherein the frame is a frame of speech.

16. The speech coder of claim 14, wherein the frame is a frame of linear prediction residue.

17. The speech coder of claim 14, wherein the target quantization is calculated in accordance with the following equation:

$U_M\equiv <br><br>\left\{U_M^n=\frac{\left(L_M^n-{\mathrm{\alpha <br><br>}}_1^n<br><br>{\hat{U}}_{M-1}^n-{\mathrm{\alpha <br><br>}}_2^n<br><br>{\hat{U}}_{M-2}^n-\mathrm{\dots <br><br>}-{\mathrm{\alpha <br><br>}}_P^n<br><br>{\hat{U}}_{M-P}^n\right)}{{\mathrm{\alpha <br><br>}}_0^n};<br><br>n=0,1,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>},N-1\right\},$

wherein {Û

<sub>M−

1</sub>ⁿ, Û

<sub>M−

2</sub>ⁿ, . . . , Û

<sub>M−

P</sub>ⁿ;

n=0,1, . . . , N−

1} are codebook entries corresponding to line spectral information parameters of the predefined number of frames processed immediately prior to the frame, and {α

₁ⁿ,α

₂ⁿ, . . . ,α

_P<sup>n;

n=</sup>0,1, . . . , N−

1} are respective parameter weights such that {α

₀ⁿ+α

₁ⁿ+, . . . , +α

_Pⁿ+1;

n=0,1, . . . , N−

1}.

18. The speech coder of claim 14, wherein the quantized line spectral information vectors are derived in accordance with the following equation:

19. The speech coder of claim 14, wherein the equivalent moving average codevectors are computed in accordance with the following equation:

${\tilde{\hat{U}}}_{M-K}\equiv <br><br>\left\{{\tilde{\hat{U}}}_{M-K}^n=\frac{\left({\hat{L}}_{M-R}^n-{\mathrm{\beta <br><br>}}_1^n<br><br>{\hat{U}}_{M-K-1}^n-{\mathrm{\beta <br><br>}}_2^n<br><br>{\hat{U}}_{M-K-2}^n-\mathrm{\dots <br><br>}-{\mathrm{\beta <br><br>}}_R^n<br><br>{\hat{U}}_{M-K-P}^n\right)}{{\mathrm{\beta <br><br>}}_0^n};<br><br>n=0,1,\mathrm{\dots <br><br>}<br><br>\text{\hspace{1em}<br><br>},N-1\right\},$

wherein {β

₁ⁿ,β

₂ⁿ, . . . ,β

_Pⁿ;

n=0,1, . . . , N−

1} are respective equivalent moving average codevector element weights such that {β

₀ⁿ+β

₁ⁿ+, . . . ,β

_Pⁿ=1;

n=0,1, . . . , N−

1}, and wherein an initial condition of {{circumflex over ({tilde over (U)})}<sub>−

1</sub>,{circumflex over ({tilde over (U)})}<sub>−

2</sub>, . . . ,{circumflex over ({tilde over (U)})}<sub>−

P</sub>} is established.

20. The speech coder of claim 14, wherein the speech coder resides in a subscriber unit of a wireless communication system.