Modeling perceptual similarity of audio signals for blind source separation evaluation

Brendan Fox*, Andrew Sabin, Bryan A Pardo, Alec Zopf

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

Existing perceptual models of audio quality, such as PEAQ, were designed to measure audio codec performance and are not well suited to evaluation of audio source separation algorithms. The relationship of many other signal quality measures to human perception is not well established. We collected subjective human assessments of distortions encountered when separating audio sources from mixtures of two to four harmonic sources. We then correlated these assessments to 18 machine-measurable parameters. Results show a strong correlation (r=0.96) between a linear combination of a subset of four of these parameters and mean human assessments. This correlation is stronger than that between human assessments and several measures currently in use.

Original languageEnglish (US)
Title of host publicationIndependent Component Analysis and Signal Separation - 7th International Conference, ICA 2007, Proceedings
PublisherSpringer Verlag
Pages454-461
Number of pages8
ISBN (Print)9783540744931
DOIs
StatePublished - 2007
Event7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007 - London, United Kingdom
Duration: Sep 9 2007Sep 12 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4666 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007
Country/TerritoryUnited Kingdom
CityLondon
Period9/9/079/12/07

Keywords

  • Audio
  • Music
  • Perceptual model
  • Source separation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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