Blind estimation of reverberation time based on the distribution of signal decay rates

被引:50
|
作者
Wen, Jimi Y. C. [1 ]
Habets, Emanuel A. P. [2 ]
Naylor, Patrick A. [1 ]
机构
[1] Imperial Coll London, Dept EEE, London, England
[2] Bar Ilan Univ, Sch Engn, IL-52100 Ramat Gan, Israel
关键词
reverberation time; blind estimation; acoustic signal analysis;
D O I
10.1109/ICASSP.2008.4517613
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
The reverberation time is one of the most prominent acoustic characteristics of an enclosure. Its value can be used to predict speech intelligibility, and is used by speech enhancement techniques to suppress reverberation. The reverberation time is usually obtained by analysing the decay rate of i) the energy decay curve that is observed when a noise source is switched off, and ii) the energy decay curve of the room impulse response. Estimating the reverberation time using only the observed reverberant speech signal, i.e., blind estimation, is required for speech evaluation and enhancement techniques. Recently, (semi) blind methods have been developed. Unfortunately, these methods are not very accurate when the source consists of a human speaker, and unnatural speech pauses are required to detect and/or track the decay. In this paper we extract and analyse the decay rate of the energy envelope blindly from the observed reverberation speech signal in the short-time Fourier transform domain. We develop a method to estimate the reverberation time using a property of the distribution of the decay rates. Experimental results using simulated and real reverberant speech signals demonstrate the performance of the new method.
引用
收藏
页码:329 / +
页数:2
相关论文
共 50 条
  • [21] Speech dereverberation based on blind estimation of a reverberation filter
    Zee, Min-Seon
    Park, Hyung-Min
    IEICE ELECTRONICS EXPRESS, 2009, 6 (20): : 1456 - 1461
  • [22] BLIND ESTIMATION OF REVERBERATION TIME USING DEEP NEURAL NETWORK
    Lee, Myungin
    Chang, Joon-Hyuk
    PROCEEDINGS OF 2016 5TH IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT (IEEE IC-NIDC 2016), 2016, : 308 - 311
  • [23] BLIND REVERBERATION TIME ESTIMATION USING A CONVOLUTIONAL NEURAL NETWORK
    Gamper, Hannes
    Tashev, Ivan J.
    2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC), 2018, : 136 - 140
  • [24] Efficient ML-Estimator for Blind Reverberation Time Estimation
    Loellmann, Heinrich W.
    Brendel, Andreas
    Kellermann, Walter
    2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2018, : 2195 - 2199
  • [25] Blind signal separation of strong reverberation based on a new algorithm
    Li, Huxiong
    Gu, Fan
    ADVANCED RESEARCH ON MECHANICAL ENGINEERING, INDUSTRY AND MANUFACTURING ENGINEERING, PTS 1 AND 2, 2011, 63-64 : 395 - +
  • [26] Deep Neural Network Based Blind Estimation of Reverberation Time Based on Multi-channel Microphones
    Lee, Myungin
    Chang, Joon-Hyuk
    ACTA ACUSTICA UNITED WITH ACUSTICA, 2018, 104 (03) : 486 - 495
  • [27] Endorsement to Audio Recorded in Different Acoustic Environment with Feature as Reverberation Time with Blind Reverberation Time Estimation Method
    Kamble, Kiran P.
    Chavan, Manik K.
    2017 INTERNATIONAL CONFERENCE ON BIG DATA, IOT AND DATA SCIENCE (BID), 2017, : 54 - 59
  • [28] On the steady-state and the transient decay methods for the estimation of reverberation time
    Sum, KS
    Pan, J
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2002, 112 (06): : 2583 - 2588
  • [29] On the steady-state and the transient decay methods for the estimation of reverberation time
    Sum, K.S.
    Pan, J.
    Journal of the Acoustical Society of America, 2002, 112 (06): : 2583 - 2588
  • [30] On Improving the Performance of a Speech Model-Based Blind Reverberation Time Estimation in Noisy Environments
    Lee, Tung-Chin
    Park, Young-Cheol
    Youn, Dae-Hee
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2014, E97A (12) : 2688 - 2692