Envelope modeling for speech and audio processing using distribution quantization

Tobias Jahnel, Tom Backstrom, Benjamin Schubert

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

5 Citations (Scopus)

Abstract

Envelope models are common in speech and audio processing: for example, linear prediction is used for modeling the spectral envelope of speech, whereas audio coders use scale factor bands for perceptual masking models. In this work we introduce an envelope model called distribution quantizer (DQ), with the objective of combining the accuracy of linear prediction and the flexibility of scale factor bands. We evaluate the performance of envelope models with respect to their ability to reduce entropy as well as their correlation to the original signal magnitude. The experiments show that in terms of entropy, distribution quantization and linear prediction are comparable, whereas for correlation, distribution quantization is better. Furthermore the coefficients of distribution quantization are independent and thus more flexible and easier to quantize than linear predictive coefficients.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherIEEE
Pages584-588
Number of pages5
ISBN (Electronic)9780992862633
DOIs
Publication statusPublished - 22 Dec 2015
MoE publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference - Nice, France
Duration: 31 Aug 20154 Sep 2015
Conference number: 23

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
CountryFrance
CityNice
Period31/08/201504/09/2015

Keywords

  • linear predictive coding
  • signal modeling
  • Speech coding

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