  Reverberation can severely degrade the quality of speech signals recorded
using microphones in an enclosure. In acoustic sensor networks with spatially
distributed microphones, a similar dereverberation performance may be achieved
using only a subset of all available microphones. Using the popular convex
relaxation method, in this paper we propose to perform microphone subset
selection for the weighted prediction error (WPE) multi-channel dereverberation
algorithm by introducing a group sparsity penalty on the prediction filter
coefficients. The resulting problem is shown to be solved efficiently using the
accelerated proximal gradient algorithm. Experimental evaluation using measured
impulse responses shows that the performance of the proposed method is close to
the optimal performance obtained by exhaustive search, both for
frequency-dependent as well as frequency-independent microphone subset
selection. Furthermore, the performance using only a few microphones for
frequency-independent microphone subset selection is only marginally worse than
using all available microphones.
