Maximum Likelihood PSD Estimation for Speech Enhancement in Reverberation and Noise

Adam Kuklasinski, Simon Doclo, Søren Holdt Jensen, Jesper Jensen

Research output: Contribution to journalJournal articleResearchpeer-review

37 Citations (Scopus)
242 Downloads (Pure)


In this contribution we focus on the problem of power spectral density (PSD) estimation from multiple microphone signals in reverberant and noisy environments. The PSD estimation method proposed in this paper is based on the maximum likelihood (ML) methodology. In particular, we derive a novel ML PSD estimation scheme that is suitable for sound scenes which besides speech and reverberation consist of an additional noise component whose second-order statistics are known. The proposed algorithm is shown to outperform an existing similar algorithm in terms of PSD estimation accuracy. Moreover, it is shown numerically that the mean squared estimation error achieved by the proposed method is near the limit set by the corresponding Cram´er-Rao lower bound. The speech dereverberation performance of a multi-channel Wiener filter (MWF) based on the proposed PSD estimators is measured using several instrumental measures and is shown to be higher than when the competing estimator is used. Moreover, we perform a speech intelligibility test where we demonstrate that both the proposed and the competing PSD estimators lead to similar intelligibility improvements.
Original languageEnglish
JournalI E E E Transactions on Audio, Speech and Language Processing
Issue number9
Pages (from-to)1599-1612
Number of pages14
Publication statusPublished - 1 Sep 2016

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