The problem of source separation, dereverberation and noise reduction using a microphone array is addressed in this paper. The observed speech is modeled by two components, namely the early speech (including the direct path and some early reflections) and the late reverberation. The minimum mean square error (MMSE) estimator of the early speech components of the various speakers is derived, which jointly suppresses the noise and the overall reverberation from all speakers. The overall time-varying level of the reverberation is estimated using two different estimators, an estimator based on a temporal model and an estimator based on a spatial model. The experimental study consists of measured acoustic transfer functions (ATFs) and directional noise with various signal-to-noise ratio levels. The separation, dereverberation and noise reduction performance is examined in terms of perceptual evaluation of speech quality (PESQ) and signal-to-interference plus noise ratio improvement.
|Title of host publication||Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings|
|Editors||Petr Tichavsky, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadege Thirion-Moreau|
|Number of pages||10|
|State||Published - 2017|
|Event||13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017 - Grenoble, France|
Duration: 21 Feb 2017 → 23 Feb 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017|
|Period||21/02/17 → 23/02/17|
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