EP1172803A2 - Système de quantification vectorielle et méthode d'opération - Google Patents

Système de quantification vectorielle et méthode d'opération Download PDF

Info

Publication number
EP1172803A2
EP1172803A2 EP01116530A EP01116530A EP1172803A2 EP 1172803 A2 EP1172803 A2 EP 1172803A2 EP 01116530 A EP01116530 A EP 01116530A EP 01116530 A EP01116530 A EP 01116530A EP 1172803 A2 EP1172803 A2 EP 1172803A2
Authority
EP
European Patent Office
Prior art keywords
lsf
estimate
prediction error
current
mean value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP01116530A
Other languages
German (de)
English (en)
Other versions
EP1172803A3 (fr
Inventor
Jonathan Alastair Gibbs
Mark Jasiuk
Alun Evans
Aaron Smith
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Motorola Solutions Inc
Original Assignee
Motorola Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Motorola Inc filed Critical Motorola Inc
Publication of EP1172803A2 publication Critical patent/EP1172803A2/fr
Publication of EP1172803A3 publication Critical patent/EP1172803A3/fr
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
    • G10L19/07—Line spectrum pair [LSP] vocoders

Definitions

  • the present invention relates to speech encoding and decoding systems in general, and more particularly to systems and methods for vector quantization of line spectral frequencies.
  • VQ vector quantization
  • MSA Microphone/Speaker Adaption
  • Aarskog et al One method for handling spectral balance variations is the Microphone/Speaker Adaption (MSA) method taught by Aarskog et al .
  • MSA Microphone/Speaker Adaption
  • This method is disadvantageous in that it requires two stages of inverse filtering, thus increasing the complexity of the quantizer due to the required autocorrelation function calculations.
  • two LPC filter quantizers are needed, one for the MSA filter and one for the conventional LPC filter.
  • An additional slow-speed data path is also needed to convey the quantized MSA filter parameters from encoder to decoder.
  • the present invention seeks to provide improved systems and methods for vector quantization that account for spectral balance variations while avoiding the limitations of the prior art.
  • a quantization system and method are disclosed that achieve similar objective performance, in terms of mean spectral distortion and outliers, for speech within and outside the training database, and similar quantizer performance for different types of speech largely irrespective of the spectral balance.
  • the present invention exploits properties of line-spectrum pairs to yield a robust quantizer with superior performance under various conditions.
  • the present invention further discloses a more error-robust system and method for deriving adaptable mean values based upon previous quantizer decisions in a uniform gain moving average fashion.
  • the present invention is an extension to mean-removed vector quantization and is equally applicable to both auto-regressive and moving average predictive vector quantization.
  • a system and method are disclosed for slow averaging of the positions of the inverse quantized line spectral frequencies (LSFs) using a series of simple filters (one per LSF) with one or more long time constants.
  • a method of providing robust quantization of speech spectral parameters tolerant to spectral balance and speaker variations including the steps of, for each of a plurality of line spectral frequencies (LSFs) of a speech spectrum, quantizing the displacement of the LSF from an estimate of its long-term mean, reconstructing an estimate of the LSF from the quantized displacement and the long-term LSF mean estimate, and filtering the reconstructed LSF estimate, thereby providing a subsequent long-term LSF mean estimate.
  • LSFs line spectral frequencies
  • the filtering step includes filtering the reconstructed LSF estimate using a first-order recursive filter.
  • the first-order recursive filter is of unity gain and employs a time constant of about 1 second for the LSF.
  • a method of quantizing speech spectral parameters that is tolerant to spectral balance and speaker variations, the method including the steps of, for each of a plurality of line spectral frequencies (LSFs) of a speech spectrum, at an encoder a) quantizing the difference between the LSF and a current LSF mean value estimate, and at the encoder and a decoder b) dequantizing the difference, c) adding the dequantized difference to a current LSF mean value estimate, thereby providing an approximation of the LSF, and d) filtering the quantized LSF together with the current LSF mean value estimate, thereby providing a new current LSF mean value estimate.
  • LSFs line spectral frequencies
  • a method of quantizing speech spectral parameters that is tolerant to spectral balance and speaker variations, the method including the steps of, for each of a plurality of line spectral frequencies (LSFs) of a speech spectrum, at an encoder a) quantizing a prediction error derived from the LSF from which a current short-term LSF mean value and a current moving average predicted LSF estimate have been subtracted, and at the encoder and a decoder b) dequantizing the prediction error, c) determining a next-current short-term LSF mean value from the dequantized prediction error and at least one previously dequantized prediction error, and d) determining a next-current moving average predicted LSF estimate from the dequantized prediction error and at least one previously dequantized prediction error.
  • LSFs line spectral frequencies
  • next-current short-term LSF mean value is the sum of a training data derived mean and a moving average of a plurality of previously dequantized prediction error values.
  • the equal gains are assigned to each dequantized prediction error value.
  • apparatus for providing robust quantization of speech spectral parameters tolerant to spectral balance and speaker variations, the apparatus including means for quantizing the displacement of a line spectral frequency (LSF) from an estimate of its long-term mean, means for reconstructing an estimate of the LSF from the quantized displacement and the long-term LSF mean estimate, and means for filtering the reconstructed LSF estimate, thereby providing a subsequent long-term LSF mean estimate.
  • LSF line spectral frequency
  • the filtering means includes a first-order recursive filter.
  • the first-order recursive filter is of unity gain and employs a time constant of about 1 second for the LSF.
  • the apparatus including an encoder including means for quantizing the difference between a line spectral frequency (LSF) and a current LSF mean value estimate, means for dequantizing the difference, means for adding the dequantized difference to a current LSF mean value estimate, thereby providing an approximation of the LSF, and means for filtering the quantized LSF together with the current LSF mean value estimate, thereby providing a new current LSF mean value estimate, and a decoder including means for dequantizing the difference, means for adding the dequantized difference to a current LSF mean value estimate, thereby providing an approximation of the LSF, and means for filtering the quantized LSF together with the current LSF mean value estimate, thereby providing a new current LSF mean value estimate.
  • LSF line spectral frequency
  • the apparatus including an encoder including means for quantizing a prediction error derived from the LSF from which a current short-term LSF mean value and a current moving average predicted LSF estimate have been subtracted, means for dequantizing the prediction error, means for determining a next-current short-term LSF mean value from the dequantized prediction error and at least one previously dequantized prediction error, and means for determining a next-current moving average predicted LSF estimate from the dequantized prediction error and at least one previously dequantized prediction error and the current short-term LSF mean value, and a decoder including means for dequantizing the prediction error, means for determining a next-current short-term LSF mean value from the dequantized prediction error and at least one previously dequantized prediction error, and means for determining a next-current moving average predicted LSF estimate from the dequantized prediction error and at least one previously dequantized prediction
  • next-current short-term LSF mean value is the sum of a training data derived mean and a moving average of a plurality of previously dequantized prediction error values.
  • the equal gains are assigned to each dequantized prediction error value.
  • Fig. 1 is a simplified illustration of a system for backwards-adaptive vector quantization of line spectral frequencies (LSF), constructed and operative in accordance with a preferred embodiment of the present invention.
  • LSFs are quantized with their previous long-term mean values removed, using any conventional VQ technique, such as memoryless, AR predictive, MA predictive, or other suitable technique.
  • the same long-term mean value is used during encoding and decoding.
  • the long-term average value of the LSF changes at both the encoder and decoder. In this way, the quantizer adapts to long-term variations in the LSFs.
  • spectral frequencies of a speech spectrum are provided to an encoder, generally referenced 10.
  • a subtractor 12 subtracts the current estimate of the mean value associated with the LSF from the LSF input.
  • a quantizer 14 then quantizes the difference of the LSF from its mean value by selecting an appropriate codebook index in accordance with any known and suitable quantization means.
  • the quantization index is then provided to inverse quantizers 16 and 18 at encoder 10 and a decoder, generally referenced 20, respectively.
  • Inverse quantizer 18 dequantizes the quantization index using any known and suitable means to determine an associated LSF.
  • An adder 22 adds the current estimate of the mean value associated with the LSF back into the LSF determined at inverse quantizer 18, thus providing an approximation of the LSF input to encoder 10.
  • the quantized LSF from adder 22 in addition to being used during subsequent speech encoding and decoding, is provided to a simple, first-order filter where the LSF is multiplied by a filter value X at a multiplier 26.
  • the previous estimate of the LSF mean value, held at a delay 28, is then multiplied by a filter value 1-X at a multiplier 30.
  • the result of multiplier 30 is then added to the result from multiplier 26 at an adder 32.
  • the result from adder 32 represents the current estimate of the LSF mean value and is stored in delay 28.
  • Inverse quantizer 16 likewise dequantizes the quantization index to determine an associated LSF which is then provided to an adder 34 and a simple, first-order filter which includes a multiplier 38, an adder 40, a delay 42, and a multiplier 44, all of which operate in the manner described hereinabove for adder 22, multiplier 26, delay 28, multiplier 30, and adder 32, with the notable exception that the estimate of the LSF mean value in delay 42 is provided to subtractor 12 in addition to being provided to adder 34.
  • Fig. 2 is a simplified illustration of a system for backwards-adaptive vector quantization of line spectral frequencies (LSF), constructed and operative in accordance with another preferred embodiment of the present invention.
  • the LSF means are derived from a relatively long moving average predictor in order to overcome the problems associated with infinite error propagation and incorporated within a conventional third-order (short) moving average predictive vector quantizer.
  • the system of Fig. 2 may be implemented using a rectangular window moving average predictor for the calculation of the LSF means, such as one that is about 750 ms long (a relatively long predictor). This may be easily achieved by employing a circular buffer containing the quantizer indices from previous decisions.
  • line spectral frequencies of a speech spectrum are provided to an encoder, generally referenced 50.
  • a subtractor 52 receives the current short-term mean value associated with the LSF from an adder 54 and subtracts it from the LSF.
  • a subtractor 56 then receives the current moving average predicted estimate of the LSF from an adder 58 and subtracts it from the output of subtractor 52.
  • the output of subtractor 56 is then divided by a tap t 0 of the short MA predictor at a divider 92 to provide a prediction error which is then quantized at a quantizer 60 using any known and suitable quantization means.
  • the quantization index is then provided to inverse quantizers 62 and 64 at encoder 50 and a decoder, generally referenced 66, respectively.
  • Inverse quantizer 62 dequantizes the quantization index using any known and suitable means to determine an associated LSF.
  • the taps of the short moving average predictor ( t 0 , t 1 & t 2 ) may be determined by any reasonable technique, but are ideally jointly optimized with the relatively long moving average LSF mean predictor in operation.
  • the current output of inverse quantizer 62 is multiplied by t 0 at a multiplier 68 and provided to an adder 70.
  • the previous output of inverse quantizer 62, stored at a delay 72, is multiplied by a tap t 1 at a multiplier 74 and provided to adder 70.
  • the twice-previous output of inverse quantizer 62, stored at a delay 76, is multiplied by a tap t 2 at a multiplier 78 and provided to adder 70.
  • Adder 70 adds all three inputs and provides the result to an adder 80.
  • the output of adder 70 represents the current quantization error component of the output LSF.
  • the previous output of inverse quantizer 62, stored at delay 72, is multiplied by tap t 1 at a multiplier 82 and provided to adder 58.
  • the twice-previous output of inverse quantizer 62, stored at delay 76, is multiplied by tap t 2 at a multiplier 84 and provided to adder 58.
  • Adder 58 adds the two inputs and provides the result to subtractor 56.
  • the output of adder 58 represents the current predicted estimate of the LSF.
  • the current output of inverse quantizer 62 is also provided to an ordered series of n delays 86, with each delay storing an n th previous output of inverse quantizer 62.
  • Each previous value n is then multiplied by 1/ n by a series of multipliers 88, thereby providing equal gain for each value n , and provided to adder 54, where they are added together with a predetermined estimate ⁇ of the mean of the LSF stored at a delay 90.
  • the value of ⁇ may be determined from training data and represents an initial estimate of the LSF means.
  • the output of adder 54 is then provided to adder 80 as well as subtractor 52.
  • the output of adder 54 represents the current short-term mean value associated with the LSF.
  • the current quantization error component of the LSF is preferably added to the current short-term LSF mean value at adder 80 to provide an approximation of the LSF input.
  • elements 68' - 90' preferably operate in the manner described hereinabove for correspondingly-numbered elements 68 - 90 with the notable exceptions that adder 54' provides input only to adder 80', delay 72' provides input only to multiplier 74', and delay 76' provides input only to multiplier 78'.
  • Fig. 3 is a simplified graph illustration showing Mean Spectral Distortion Performance (dB) of a conventional third-order MA Predictive LSF VQ with Fixed Means, represented by a plot 100, the Moving Average Mean Adaptation of Fig. 2, represented by a plot 102, and the Backwards Adapted Means of Fig. 1, represented by a plot 104.
  • dB Mean Spectral Distortion Performance
  • FIG. 3 shows the spectral distortion figures for three identical third-order moving average predictive quantizers (MA-PVQs) plotted with and without adaptation of the mean values as described hereinabove.
  • the test file that was used comprised 8,000 frames each of flat filtered speech, Intermediate Reference System (IRS) filtered speech, and modified IRS filtered speech.
  • the training data that was used for both quantizers was IRS filtered.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
EP01116530A 2000-07-13 2001-07-09 Système de quantification vectorielle et méthode d'opération Withdrawn EP1172803A3 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB0017145 2000-07-13
GB0017145A GB2364870A (en) 2000-07-13 2000-07-13 Vector quantization system for speech encoding/decoding

Publications (2)

Publication Number Publication Date
EP1172803A2 true EP1172803A2 (fr) 2002-01-16
EP1172803A3 EP1172803A3 (fr) 2004-01-14

Family

ID=9895545

Family Applications (1)

Application Number Title Priority Date Filing Date
EP01116530A Withdrawn EP1172803A3 (fr) 2000-07-13 2001-07-09 Système de quantification vectorielle et méthode d'opération

Country Status (2)

Country Link
EP (1) EP1172803A3 (fr)
GB (1) GB2364870A (fr)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5966688A (en) * 1997-10-28 1999-10-12 Hughes Electronics Corporation Speech mode based multi-stage vector quantizer

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3033060B2 (ja) * 1988-12-22 2000-04-17 国際電信電話株式会社 音声予測符号化・復号化方式
US5664053A (en) * 1995-04-03 1997-09-02 Universite De Sherbrooke Predictive split-matrix quantization of spectral parameters for efficient coding of speech
US6081776A (en) * 1998-07-13 2000-06-27 Lockheed Martin Corp. Speech coding system and method including adaptive finite impulse response filter

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5966688A (en) * 1997-10-28 1999-10-12 Hughes Electronics Corporation Speech mode based multi-stage vector quantizer

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
AARSKOG A: "A LONG-TERM PREDICTIVE ADPCM CODER WITH SHORT-TERM PREDICTION AND VECTOR QUANTIZATION" IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH & SIGNAL PROCESSING (ICASSP'91), TORONTO, CANADA, vol. 1, 14 - 17 May 1991, pages 37-40, XP000245161 IEEE, New York, USA ISBN: 0-7803-0003-3 *
CHO INHWAN ET AL: "Predictive pyramid vector quantisation of LSF parameters" ELECTRONICS LETTERS, IEE STEVENAGE, GB, vol. 34, no. 8, 16 April 1998 (1998-04-16), pages 735-736, XP006009612 ISSN: 0013-5194 *
OHMURO H ET AL: "VECTOR QUANTIZATION OF LSP PARAMETERS USING MOVING AVERAGE INTERFRAME PREDICTION" ELECTRONICS & COMMUNICATIONS IN JAPAN, PART III - FUNDAMENTAL ELECTRONIC SCIENCE, SCRIPTA TECHNICA. NEW YORK, US, vol. 77, no. 10, PART 3, 1 October 1994 (1994-10-01), pages 12-25, XP000527379 ISSN: 1042-0967 *
SKOGLUND J ET AL: "PREDICTIVE VQ FOR NOISY CHANNEL SPECTRUM CODING: AR OR MA?" IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP'97), MUNICH, GERMANY, vol. 2, 21 - 24 April 1997, pages 1351-1354, XP000822706 IEEE COMP. SOC. PRESS, USA ISBN: 0-8186-7920-4 *

Also Published As

Publication number Publication date
GB2364870A (en) 2002-02-06
GB0017145D0 (en) 2000-08-30
EP1172803A3 (fr) 2004-01-14

Similar Documents

Publication Publication Date Title
EP0503684B1 (fr) Procédé de filtrage adaptatif de la parole et de signaux audio
CA2483791C (fr) Procede et dispositif de masquage efficace d'effacement de trames dans des codec vocaux de type lineaire predictif
EP0939394B1 (fr) Dispositif de codage de la parole et de la musique et dispositif de décodage
US5699477A (en) Mixed excitation linear prediction with fractional pitch
US5140638A (en) Speech coding system and a method of encoding speech
EP0732686B1 (fr) Codage CELP à 32 kbit/s à faible retard d'un signal à large bande
EP1202251A2 (fr) Transcodeur empêchant le codage en cascade de signaux vocaux
EP0501421B1 (fr) Système de codage de parole
US6694018B1 (en) Echo canceling apparatus and method, and voice reproducing apparatus
KR20090039660A (ko) 패킷 손실 은닉 후 디코더 상태의 갱신 기법
NO340674B1 (no) Koding av informasjonssignal
CA2262787C (fr) Procedes et dispositifs pour conditionner le bruit de signaux representatifs des informations audio sous forme comprimee et numerisee
US5913187A (en) Nonlinear filter for noise suppression in linear prediction speech processing devices
JPH02155313A (ja) 符号化方法
JP3357795B2 (ja) 音声符号化方法および装置
EP1301018A1 (fr) Méthode et appareille pour modifié un signal digital dons un domain codifié
US6205423B1 (en) Method for coding speech containing noise-like speech periods and/or having background noise
WO2001002929A2 (fr) Gestion du bruit du domaine code
US20130268268A1 (en) Encoding of an improvement stage in a hierarchical encoder
EP1172803A2 (fr) Système de quantification vectorielle et méthode d'opération
Härmä et al. Backward adaptive warped lattice for wideband stereo coding
Lee An enhanced ADPCM coder for voice over packet networks
JPH04301900A (ja) 音声符号化装置
KR100392258B1 (ko) Ｃｅｌｐ 보코더의 처리 지연시간을 감소하기 위한 인코딩및 디코딩 블럭 구조 및 그 구조를 이용한 인코딩 및디코딩 방법
Aarskog et al. Predictive coding of speech using microphone/speaker adaptation and vector quantization

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

AK Designated contracting states

Kind code of ref document: A2

Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE TR

AX Request for extension of the european patent

Free format text: AL;LT;LV;MK;RO;SI

PUAL Search report despatched

Free format text: ORIGINAL CODE: 0009013

AK Designated contracting states

Kind code of ref document: A3

Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE TR

AX Request for extension of the european patent

Extension state: AL LT LV MK RO SI

AKX Designation fees paid
REG Reference to a national code

Ref country code: DE

Ref legal event code: 8566

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20040715

P01 Opt-out of the competence of the unified patent court (upc) registered

Effective date: 20230520