A Contrast Function Based on Generalized Divergences for Solving the Permutation Problem in Convolved Speech Mixtures

Auxiliadora Sarmiento, Ivan Duran-Diaz, Andrzej Cichocki, Sergio Cruces

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

In this paper, we propose a method for solving the permutation problem that is inherent in the separation of convolved mixtures of speech signals in the time-frequency domain. The proposed method obtains the solution through maximization of a contrast function that exploits the similarity of the temporal envelope of the speech spectrum. For this purpose, the contrast calculation uses a global measure of similarity based on the recently developed family of generalized Alpha-Beta divergences, which depend on two tuning parameters, alpha and beta. This parameterization is exploited to best measure the similarity of the speech spectrum and to obtain solutions that are robust against noise and outliers. The ability of this contrast function to solve the permutation problem is supported by a theoretical study that shows that for a simple time-frequency speech model, the contrast value reaches its maximum when the estimated components are properly aligned. Several performance studies demonstrate that the proposed method maintains a high level of permutation correction accuracy in a wide variety of acoustic environments. Moreover, it produces better results than other state-of-the-art methods for solving permutations in highly reverberant environments.

Original languageEnglish
Article number7128368
Pages (from-to)1713-1726
Number of pages14
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume23
Issue number11
DOIs
Publication statusPublished - 1 Nov 2015
Externally publishedYes

Keywords

  • Blind source separation (BSS)
  • permutation problem
  • speech enhancement

Fingerprint

Dive into the research topics of 'A Contrast Function Based on Generalized Divergences for Solving the Permutation Problem in Convolved Speech Mixtures'. Together they form a unique fingerprint.

Cite this