Late reverberant spectral variance estimation based on a statistical model

Emanuël A.P. Habets, Sharon Gannot, Israel Cohen

    Research output: Contribution to journalArticlepeer-review

    139 Scopus citations

    Abstract

    In speech communication systems the received microphone signals are degraded by room reverberation and ambient noise that decrease the fidelity and intelligibility of the desired speaker. Reverberant speech can be separated into two components, viz. early speech and late reverberant speech. Recently, various algorithms have been developed to suppress late reverberant speech. One of the main challenges is to develop an estimator for the so-called late reverberant spectral variance (LRSV) which is required by most of these algorithms. In this letter a statistical reverberation model is proposed that takes the energy contribution of the direct-path into account. This model is then used to derive a more general LRSV estimator, which in a particular case reduces to an existing LRSV estimator. Experimental results show that the developed estimator is advantageous in case the source-microphone distance is smaller than the critical distance.

    Original languageEnglish
    Article number2024791
    Pages (from-to)770-773
    Number of pages4
    JournalIEEE Signal Processing Letters
    Volume16
    Issue number9
    DOIs
    StatePublished - 2009

    Bibliographical note

    Funding Information:
    Manuscript received January 18, 2009; revised April 09, 2009. First published June 10, 2009; current version published July 09, 2009. This work was supported by the Israel Science Foundation (Grant 1085/05). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Kainam Thomas Wong.

    Keywords

    • Reverberation suppression
    • Speech enhancement
    • Statistical room acoustics

    Fingerprint

    Dive into the research topics of 'Late reverberant spectral variance estimation based on a statistical model'. Together they form a unique fingerprint.

    Cite this