Nonparametric bayesian dereverberation of power spectrograms based on infinite-order autoregressive processes

Akira Maezawa, Katsutoshi Itoyama, Kazuyoshi Yoshii, Hiroshi G. Okuno

    研究成果: Article査読

    5 被引用数 (Scopus)


    This paper describes a monaural audio dereverberation method that operates in the power spectrogram domain. The method is robust to different kinds of source signals such as speech or music. Moreover, it requires little manual intervention, including the complexity of room acoustics. The method is based on a non-conjugate Bayesian model of the power spectrogram. It extends the idea of multi-channel linear prediction to the power spectrogram domain, and formulates a model of reverberation as a non-negative, infinite-order autoregressive process. To this end, the power spectrogram is interpreted as a histogram count data, which allows a nonparametric Bayesian model to be used as the prior for the autoregressive process, allowing the effective number of active components to grow, without bound, with the complexity of data. In order to determine the marginal posterior distribution, a convergent algorithm, inspired by the variational Bayes method, is formulated. It employs the minorization-maximization technique to arrive at an iterative, convergent algorithm that approximates the marginal posterior distribution. Both objective and subjective evaluations show advantage over other methods based on the power spectrum.We also apply the method to amusic information retrieval task and demonstrate its effectiveness.

    ジャーナルIEEE/ACM Transactions on Speech and Language Processing
    出版ステータスPublished - 2014 12月 1

    ASJC Scopus subject areas

    • 信号処理
    • 電子工学および電気工学
    • メディア記述
    • 音響学および超音波学
    • 器械工学
    • 言語学および言語
    • 言語聴覚療法


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