NDLI: A contrast function based on generalized divergences for solving the permutation problem in convolved speech mixtures

Content Provider	ACM Digital Library
Author	Durán-díaz, Iván Cruces, Sergio Sarmiento, Auxiliadora Cichocki, Andrzej
Copyright Year	2015
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.
Starting Page	1713
Ending Page	1726
Page Count	14
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2015.2447281
Volume Number	23
Issue Number	11
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Blind source separation (BSS) Permutation problem Speech enhancement
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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