Comparison of supervised and semi-supervised beamformers using real audio recordings

In this contribution two different disciplines for designing microphone array beamformers are explored. On the one hand a fixed beamformer based on numerical near field optimization is employed. On the other hand an adaptive beamformer algorithm based on the linearly constrained minimum variance (LCMV) method is applied. For the evaluation, an audio-database for microphone array impulse responses and audio recordings (speech and noise) was created. Different acoustic scenarios were constructed, consisting of various audio sources (desired speaker, interfering speaker and directional noise) distributed around the microphone array at different angles and distances. The algorithms were compared based on both objective measure (signal-to-noise, signal-to-interference and speech distortion, and subjective tests (assessment of sonograms and informal listening tests).