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Title: Nonparametric Bayesian Dereverberation of Power Spectrograms Based on Infinite-Order Autoregressive Processes
Authors: Maezawa, Akira
Itoyama, Katsutoshi
Yoshii, Kazuyoshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Okuno, Hiroshi G.
Author's alias: 前澤, 陽
糸山, 克寿
吉井, 和佳
奥乃, 博
Keywords: Dereverberation
Nonparameteric Bayes
Minorization Maximization
Issue Date: Dec-2014
Publisher: IEEE
Journal title: IEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume: 22
Issue: 12
Start page: 1918
End page: 1930
Abstract: 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 a music information retrieval task and demonstrate its effectiveness.
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
DOI(Published Version): 10.1109/TASLP.2014.2355772
Appears in Collections:Journal Articles

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