  Recently, the research on ad-hoc microphone arrays with deep learning has
drawn much attention, especially in speech enhancement and separation. Because
an ad-hoc microphone array may cover such a large area that multiple speakers
may locate far apart and talk independently, target-dependent speech
separation, which aims to extract a target speaker from a mixed speech, is
important for extracting and tracing a specific speaker in the ad-hoc array.
However, this technique has not been explored yet. In this paper, we propose
deep ad-hoc beamforming based on speaker extraction, which is to our knowledge
the first work for target-dependent speech separation based on ad-hoc
microphone arrays and deep learning. The algorithm contains three components.
First, we propose a supervised channel selection framework based on speaker
extraction, where the estimated utterance-level SNRs of the target speech are
used as the basis for the channel selection. Second, we apply the selected
channels to a deep learning based MVDR algorithm, where a single-channel
speaker extraction algorithm is applied to each selected channel for estimating
the mask of the target speech. We conducted an extensive experiment on a
WSJ0-adhoc corpus. Experimental results demonstrate the effectiveness of the
proposed method.
