Proceedings Article10.1109/ICASSP.2012.6287806
An expectation-maximization algorithm for multichannel adaptive speech dereverberation in the frequency-domain
Dominic Schmid,Sarmad Malik,Gerald Enzner +2 more
- 25 Mar 2012
- pp 17-20
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TL;DR: This paper forms an overlap-save observation model for the multichannel blind problem in the DFT-domain and derives an iterative ML algorithm for blind equalization and channel identification (ML-BENCH) which comprises two distinct and coupled subsystems.
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Abstract: This paper presents an online dereverberation algorithm that is derived within the maximum-likelihood expectation-maximization (ML-EM) framework. We formulate an overlap-save observation model for the multichannel blind problem in the DFT-domain. The modeling of acoustic channel impulse responses as random variables with a first-order Markov property facilitates the ensuing algorithm to cope with time-varying conditions. We then show that the ML-EM learning rules for the multichannel state-space model at hand take the form of a recursive posterior estimator for the channels, followed by an equalization stage for recovering the speech signal subject to an expectation with respect to the estimated channel posterior. Our derivation thus results in an iterative ML algorithm for blind equalization and channel identification (ML-BENCH) which comprises two distinct and coupled subsystems. The dereverberation performance of the proposed system is evaluated by considering spectrograms and instrumental quality measures.
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Citations
Variational Bayesian inference for multichannel dereverberation and noise reduction
TL;DR: This work addresses the problem of combined speech dereverberation and noise reduction using a variational Bayesian (VB) inference approach that relies on a multichannel state-space model for the acoustic channels that combines frame-based observation equations in the frequency domain with a first-order Markov model to describe the time-varying nature of the room impulse responses.
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Speech dereverberation using weighted prediction error with laplacian model of the desired signal
Ante Jukic,Simon Doclo +1 more
- 04 May 2014
TL;DR: Experimental results, obtained using measured impulse responses, indicate that the proposed approach could be used to improve the dereverberation performance compared to the classical technique.
An expectation-maximization algorithm for multimicrophone speech dereverberation and noise reduction with coherence matrix estimation
TL;DR: In this paper, a novel algorithm to simultaneously suppress early reflections, late reverberation, and ambient noise is presented and it is shown that significant improvement is obtained and that the proposed algorithm outperforms baseline single-channel and multichannel dereverberation algorithms, as well as a state-of-the-art multich channel derever beration algorithm.
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Speech dereverberation using weighted prediction error with correlated inter-frame speech components
TL;DR: It is shown that, given an estimate of the IFC matrix, the proposed approach results in a convex quadratic optimization problem with respect to the reverberation prediction weights, and a closed-form solution can be accordingly derived.
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•Proceedings Article
Multi-microphone speech dereverberation using expectation-maximization and Kalman smoothing
Boaz Schwartz,Sharon Gannot,Emanuel A. P. Habets +2 more
- 01 Sep 2013
TL;DR: A multi-microphone algorithm that simultaneously estimates the acoustic system and the clean signal is proposed and experimental results show a significant dereverberation capabilities of the proposed algorithm with only low speech distortion.
15
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