Relative transfer function identification using convolutive transfer function approximation

Ronen Talmon, Israel Cohen, Sharon Gannot

    Research output: Contribution to journalArticlepeer-review

    96 Scopus citations

    Abstract

    In this paper, we present a relative transfer function (RTF) identification method for speech sources in reverberant environments. The proposed method is based on the convolutive transfer function (CTF) approximation, which enables to represent a linear convolution in the time domain as a linear convolution in the short-time Fourier transform (STFT) domain. Unlike the restrictive and commonly used multiplicative transfer function (MTF) approximation, which becomes more accurate when the length of a time frame increases relative to the length of the impulse response, the CTF approximation enables representation of long impulse responses using short time frames. We develop an unbiased RTF estimator that exploits the nonstationarity and presence probability of the speech signal and derive an analytic expression for the estimator variance. Experimental results show that the proposed method is advantageous compared to common RTF identification methods in various acoustic environments, especially when identifying long RTFs typical to real rooms.

    Original languageEnglish
    Article number4802172
    Pages (from-to)546-555
    Number of pages10
    JournalIEEE Transactions on Audio, Speech and Language Processing
    Volume17
    Issue number4
    DOIs
    StatePublished - May 2009

    Bibliographical note

    Funding Information:
    Manuscript received July 09, 2008; revised October 24, 2008. Current version published March 18, 2009. This work was supported by the Israel Science Foundation under Grant 1085/05. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Vesa Valimaki. R. Talmon and I. Cohen are with the Department of Electrical Engineering, The Technion—Israel Institute of Technology, Haifa 32000, Israel (e-mail: ronenta2@techunix.technion.ac.il; icohen@ee.technion.ac.il). S. Gannot is with the School of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel (e-mail: gannot@eng.biu.ac.il). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TASL.2008.2009576

    Keywords

    • Acoustic noise measurement
    • Adaptive signal processing
    • Array signal processing
    • Speech enhancement
    • System identification

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