US5822723A
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Encoding and decoding method for linear predictive coding (LPC) coefficient
Assignee: Samsung Electronics Co Ltd
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Filing: 1996-09-24
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Granted: 1998-10-13
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Expires: 2016-09-24
Abstract
Abstract A speech signal encoding/decoding method is provided. The method of encoding LPC coefficients includes dividing the nth-order line spectral frequencies into lower, middle and upper code vectors, quantizing the middle code vectors using a middle code book to generate a first index, selecting one of a plurality of lower code books according to the lowermost line spectral frequency of the middle code vector and the line spectral frequencies of the lower code vectors, and quantizing the lower code vectors using the selected lower code book to generate a second index, selecting one of a plurality of upper code books according to the uppermost line spectral frequency of the middle code vector and the line spectral frequencies of the upper code vectors, quantizing the upper code vectors using the selected upper code book to generate a third index, and transmitting the first, second and third indexes. In the above quantization, the line spectral frequencies are quantized using a linked split vector quantization (LSVQ), and the search of the code book is efficiently performed, so that the spectral distortion and outlier percentages are lower at 23 bits/frame than those of the split vector quantization (SVQ) at 24 bits/frame.
Inventors
- 👤 Moo-young Kim
- 👤 Nam-kyu Ha
- 👤 Sang-ryong Kim
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-05 10:52
CPC Classifications
H; H03; H03M; H03M7/00; H03M7/30; G; G10; G10L; G10L19/00; G10L19/04; G10L19/06; G10L19/07
Claims Analysis
Total Claims: 13
Claim 1
Independent Claim
152 words
A code book training method for vector-quantizing nth-order line spectral frequencies of an input speech signal, the code book training method comprising: performing linear predictive analysis of an input speech signal to produce a linear predictive encoding coefficient; converting the linear predictive encoding coefficient into line spectral frequencies of an nth-order; dividing the nth-order line spectral frequencies into a plurality of lower, middle, and upper code vectors; training the middle code vectors with a middle code book; training the lower code vectors with a plurality of lower code books according to a relationship between a lowermost line spectral frequency of the middle code vectors and the line spectral frequencies of the lower code vectors; and training the upper code vectors with a plurality of upper code books according to a relationship between an uppermost line spectral frequency of the middle code vectors and the line spectral frequencies of the upper code vectors.
Claim 2
Dependent Claim
152 words
A code book training method for vector-quantizing nth-order line spectral frequencies of an input speech signal, the code book training method comprising: performing linear predictive analysis of an input speech signal to produce a linear predictive encoding coefficient; converting the linear predictive encoding coefficient into line spectral frequencies of an nth-order; dividing the nth-order line spectral frequencies into a plurality of lower, middle, and upper code vectors; training the middle code vectors with a middle code book; training the lower code vectors with a plurality of lower code books according to a relationship between a lowermost line spectral frequency of the middle code vectors and the line spectral frequencies of the lower code vectors; and training the upper code vectors with a plurality of upper code books according to a relationship between an uppermost line spectral frequency of the middle code vectors and the line spectral frequencies of the upper code vectors.
Claim 3
Dependent Claim
29 words
The code book training method as claimed in claim 1 comprising allocating more bits per frame to the middle code book than to the lower and upper code books.
Claim 4
Dependent Claim
23 words
The code book training method as claimed in claim 1, wherein training the middle code vectors includes performing a Linde, Buzo, Gray algorithm.
Claim 5
Dependent Claim
86 words
The code book training method as claimed in claim 1, wherein training the lower code vectors comprises: classifying a range of the lowermost line spectral frequency of the middle code vectors into a plurality of classes; and training the lower code vectors with a number of lower code books corresponding to a number of classes according to a joint probability distribution between the lowermost line spectral frequencies of the middle code vectors corresponding to the classes and the line spectral frequencies of the lower code vectors.
Claim 6
Dependent Claim
57 words
The code book training method as claimed in claim 4, wherein classifying the range of the lowermost line spectral frequency of the middle code vectors includes selecting the range of the lowermost line spectral frequency of the middle code vectors so that the cumulative probability distributions of the middle code vectors are the same in each class.
Claim 7
Dependent Claim
86 words
The code book training method as claimed in claim 1, wherein training the upper code vectors comprises: classifying a range of the uppermost line spectral frequency of the middle code vectors into a plurality of classes; and training the upper code vectors with a number of upper code books corresponding to a number of classes according to a joint probability distribution between the uppermost line spectral frequency of the middle code vectors corresponding to the classes and the line spectral frequencies of the upper code vectors.
Claim 8
Dependent Claim
57 words
The code book training method as claimed in claim 6, wherein classifying the range of the uppermost line spectral frequency of the middle code vectors includes selecting the range of the uppermost line spectral frequency of the middle code vectors so that the cumulative probability distributions of the middle code vectors are the same in each class.
Claim 9
Independent Claim
172 words
A method of encoding a speech signal comprising: performing linear predictive analysis of an input speech signal to produce a linear predictive encoding coefficient; converting the linear predictive encoding coefficient into line spectral frequencies of an nth-order; dividing the nth-order line spectral frequencies into a plurality of lower, middles and upper code vectors; quantizing the middle code vectors using a middle code book to generate a first index; selecting one of a plurality of lower code books according to a lowermost line spectral frequency of the middle code vector and the line spectral frequencies of the lower code vectors, and quantizing the lower code vectors using the selected lower code book to generate a second index; selecting one of a plurality of upper code books according to the uppermost line spectral frequency of the middle code vector and the line spectral frequencies of the upper code vectors, and quantizing the upper code vectors using the selected upper code book to generate a third index; and transmitting the first, second and third indexes.
Claim 10
Independent Claim
172 words
A method of encoding a speech signal comprising: performing linear predictive analysis of an input speech signal to produce a linear predictive encoding coefficient; converting the linear predictive encoding coefficient into line spectral frequencies of an nth-order; dividing the nth-order line spectral frequencies into a plurality of lower, middles and upper code vectors; quantizing the middle code vectors using a middle code book to generate a first index; selecting one of a plurality of lower code books according to a lowermost line spectral frequency of the middle code vector and the line spectral frequencies of the lower code vectors, and quantizing the lower code vectors using the selected lower code book to generate a second index; selecting one of a plurality of upper code books according to the uppermost line spectral frequency of the middle code vector and the line spectral frequencies of the upper code vectors, and quantizing the upper code vectors using the selected upper code book to generate a third index; and transmitting the first, second and third indexes.
Claim 11
Dependent Claim
120 words
The method of claim 8, wherein quantizing the upper, middle, and lower code vectors includes determining a weighted Euclidean distance measure d(ω,ω) for obtaining a nearest code vector for a code vector being quantized, wherein the weighted Euclidean distance measure d(ω,ω) is obtained from ##EQU6## wherein ω represents initial line spectral frequencies before the quantization, ω represents values of code vectors stored in the middle code book after quantization, ω i and ω represent ith line spectral frequencies before and after quantization, respectively, and v(i) represents a variable weight function of the ith line spectral frequency, obtained from ##EQU7## wherein ω 0 =0, ω P+1 =f S /2, and f S is a sampling frequency for the input speech signal.
Claim 12
Independent Claim
169 words
A method of decoding a speech signal encoded as first, second, and third indexes generated by dividing nth-order line spectral frequency coefficients of the speech signal into lower, middle, and upper code vectors and quantizing the divided code vectors into the line spectral frequency coefficients, the method comprising: selecting a code vector corresponding to the first index using a middle code book to generate quantized middle code vectors; selecting one of a plurality of lower code books according to a lowermost line spectral frequency of the middle code vectors and selecting a code vector corresponding to the second index using the selected lower code book to generate quantized lower code vectors; selecting one of a plurality of upper code books according to the uppermost line spectral frequency of the middle code vectors and selecting a code vector corresponding to the third index using the selected upper code books to generate quantized upper code vectors; and reconstructing an input speech signal from the quantized lower, middle, and upper code vectors.
Claim 13
Independent Claim
169 words
A method of decoding a speech signal encoded as first, second, and third indexes generated by dividing nth-order line spectral frequency coefficients of the speech signal into lower, middle, and upper code vectors and quantizing the divided code vectors into the line spectral frequency coefficients, the method comprising: selecting a code vector corresponding to the first index using a middle code book to generate quantized middle code vectors; selecting one of a plurality of lower code books according to a lowermost line spectral frequency of the middle code vectors and selecting a code vector corresponding to the second index using the selected lower code book to generate quantized lower code vectors; selecting one of a plurality of upper code books according to the uppermost line spectral frequency of the middle code vectors and selecting a code vector corresponding to the third index using the selected upper code books to generate quantized upper code vectors; and reconstructing an input speech signal from the quantized lower, middle, and upper code vectors.
References Cited (Backward Citations)
US5384891A
Cited
US5012518A
Cited
US5151968A
Cited
US5487128A
Cited
US5677986A
Cited
US5682407A
Cited
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