An experimental comparison of source separation and beamforming techniques for microphone array signal enhancement

Joachim Thiemann 1, 2 Emmanuel Vincent 3, 4
1 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
3 PAROLE - Analysis, perception and recognition of speech
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
4 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We consider the problem of separating one or more speech signals from a noisy background. Although blind source separation (BSS) and beamforming techniques have both been exploited in this context, the former have typically been applied to small microphone arrays and the latter to larger arrays. In this paper, we provide an experimental comparison of some established beamforming and post-filtering techniques on the one hand and modern BSS techniques involving advanced spectral models on the other hand. We analyze the results as a function of the number of microphones, the number of speakers and the input Signal-to-Noise Ratio (iSNR) w.r.t. multichannel real-world environmental noise recordings. The results of the comparison show that, provided that a suitable post-filter or spectral model is chosen, beamforming performs similar to BSS on average in the single-speaker case while in the two-speaker case BSS exceeds beamformer performance. Crucially, this claim holds independently of the number of microphones.
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Communication dans un congrès
MLSP - 23rd IEEE International Workshop on Machine Learning for Signal Processing - 2013, Sep 2013, Southampton, United Kingdom. 2013
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Joachim Thiemann, Emmanuel Vincent. An experimental comparison of source separation and beamforming techniques for microphone array signal enhancement. MLSP - 23rd IEEE International Workshop on Machine Learning for Signal Processing - 2013, Sep 2013, Southampton, United Kingdom. 2013. 〈hal-00850173〉

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