Decorrelated innovative Codebooks for ACELP using factorization of autocorrelation matrix

Tom Bäckström*, Christian R. Helmrich

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Modern speech codecs based on Code Excited Linear Prediction (CELP) employ an analysis-by-synthesis optimization loop to find the best quantization of the source model parameters. With this approach, optimal quantization can be achieved only with an exhaustive search. Instead, we propose to use matrix factorization to decorrelate the objective function of the optimization problem, whereby the computationally expensive iteration can be avoided and optimal performance is guaranteed. We compare two factorizations of the autocorrelation matrix, the eigenvalue decomposition and Vandermonde factorization. Our experiments show that decorrelation improves perceptual SNR and gives a large reduction in computational complexity, mostly without significant impact on subjective quality.

Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association
Pages2794-2798
Number of pages5
Publication statusPublished - 1 Jan 2014
MoE publication typeA4 Article in a conference publication
EventInterspeech - Singapore, Singapore
Duration: 14 Sep 201418 Sep 2014

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherInternational Speech Communication Association
ISSN (Print)2308-457X

Conference

ConferenceInterspeech
CountrySingapore
CitySingapore
Period14/09/201418/09/2014

Keywords

  • ACELP
  • Eigen-decomposition
  • Innovative codebook
  • Speech coding
  • Vandermonde factorization

Fingerprint Dive into the research topics of 'Decorrelated innovative Codebooks for ACELP using factorization of autocorrelation matrix'. Together they form a unique fingerprint.

Cite this