Performance measurement in blind audio source separation

Queen Mary University of London · Institut de Recherche en Informatique et Systèmes Aléatoires · +1 more institution

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Abstract

In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four different sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated source into a true source part plus error terms corresponding to interferences, additive noise, and algorithmic artifacts. Then, we derive a global performance measure using an energy ratio, plus a separate performance measure for each error term. These measures are computed and discussed on the results of several BASS problems with various…

Citation impact

2,938
total citations
FWCI
35.55
Percentile
100%
References
21
Citations per year

Authors

3

Topics & keywords

Keywords
  • Blind signal separation
  • Computer science
  • Source separation
  • Bass (fish)
  • Measure (data warehouse)
  • Source model
  • Algorithm
  • Speech recognition
UN Sustainable Development Goals
  • Affordable and clean energy
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