Successive relative transfer function identification using single microphone speech enhancement

Dani Cherkassky, Shlomo E. Chazan, Jacob Goldberger, Sharon Gannot

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

1 Scopus citations

Abstract

A distortionless speech extraction in a reverberant environment can be achieved by an application of a beamforming algorithm, provided that the relative transfer functions (RTFs) of the sources and the covariance matrix of the noise are known. In this contribution, we consider the RTF identification challenge in a multi-source scenario. We propose a successive RTF identification (SRI), based on a sole assumption that sources become successively active. The proposed algorithm identifies the RTF of the ith speech source assuming that the RTFs of all other sources in the environment and the power spectral density (PSD) matrix of the noise were previously estimated. The proposed RTF identification algorithm is based on the neural network Mix-Max (NN-MM) single microphone speech enhancement algorithm, followed by a least-squares (LS) system identification method. The proposed RTF estimation algorithm is validated by simulation.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1235-1239
Number of pages5
ISBN (Electronic)9780992862671
DOIs
StatePublished - 23 Oct 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sep 2017

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017
Volume2017-January

Conference

Conference25th European Signal Processing Conference, EUSIPCO 2017
Country/TerritoryGreece
CityKos
Period28/08/172/09/17

Bibliographical note

Publisher Copyright:
© EURASIP 2017.

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