Predictive split-matrix quantization of spectral parameters for efficient coding of speech

• US 5,664,053 A
• Filed: 04/03/1995
• Issued: 09/02/1997
• Est. Priority Date: 04/03/1995
• Status: Expired due to Term

• Current Assignee: Universite De Strasbourg (Financière Meeschaert SA)
• Original Assignee: Universite De Strasbourg (Financière Meeschaert SA)
• Inventors: Laflamme, Claude, Salami, Redman, Adoul, Jean-Pierre
• Primary Examiner(s): MacDonald, Allen R.
• Assistant Examiner(s): Chawan, Vijay B.
• Application Number:
  US08/416,019
• Time in Patent Office: 883 Days
• Field of Search: 395/2, 395/2.31, 395/2.29, 395/2.28, 395/2.1, 395/2.38, 395/2.39, 381/31, 381/36, 381/34, 381/37, 381/40, 381/41, 381/51, 381/38, 381/47, 381/49
• US Class Current: 704/219
• CPC Class Codes: G10L 19/06 Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
• CPC Class Codes: G10L 2019/0004 Design or structure of the codebook

Abstract

The present invention concerns efficient quantization of more than one LPC spectral models per frame in order to enhance the accuracy of the time-varying spectrum representation without compromising on the coding-rate. Such efficient representation of LPC spectral models is advantageous to a number of techniques used for digital encoding of speech and/or audio signals.

Claims

1. A method for jointly quantizing N linear-predictive-coding spectral models per frame of a sampled sound signal, in which N>

1, in view of enhancing a spectral-accuracy/coding-rate trade-off in a technique for digitally encoding said sound signal, said method comprising the following combination of steps;

(a) forming a matrix, F, comprising N rows defining N vectors representative of said N linear-predictive-coding spectral models, respectively;

(b) removing from the matrix F a time-varying prediction matrix, P, based on at least one previous frame, to obtain a residual matrix, R; and

(c) vector quantizing said residual matrix R.

2. A method as defined in claim 1, wherein, to reduce the complexity of vector quantizing said residual matrix R, step (c) comprises the steps of partitioning said residual matrix R into a number of q sub matrices, having N rows, and vector quantizing independently each sub matrix.

3. A method as defined in claim 1, comprising the step of obtaining said time-varying prediction matrix P using a non-recursive prediction approach.

4. A method as defined in claim 3, wherein said non-recursive prediction approach consists of calculating the time-varying prediction matrix P according to the following formula,

5. A method as defined in claim 1, wherein said N linear-predictive-coding spectral models per frame correspond to N sub frames interspersed with m-1 sub frames, m being an integer, and wherein said vectors representative of said linear-predictive-coding spectral models corresponding to said interspersed sub frames are obtained using linear interpolation.

6. A method as defined in claim 1, further comprising the step of obtaining the time-varying prediction matrix P using a recursive prediction approach.

7. A method as defined in claim 1, wherein said N linear-predictive-coding spectral models per frame results from a linear-predictive-coding analysis using different window shapes according to the order of a particular spectral model within the frame.