An Experimental Comparison Of Source Separation And Beamforming Techniques For Microphone Array Signal Enhancement
Joachim Thiemann, Emmanuel Vincent

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.