  This study considers the problem of detecting and locating an active talker's
horizontal position from multichannel audio captured by a microphone array. We
refer to this as active speaker detection and localization (ASDL). Our goal was
to investigate the performance of spatial acoustic features extracted from the
multichannel audio as the input of a convolutional recurrent neural network
(CRNN), in relation to the number of channels employed and additive noise. To
this end, experiments were conducted to compare the generalized
cross-correlation with phase transform (GCC-PHAT), the spatial cue-augmented
log-spectrogram (SALSA) features, and a recently-proposed beamforming method,
evaluating their robustness to various noise intensities. The array aperture
and sampling density were tested by taking subsets from the 16-microphone
array. Results and tests of statistical significance demonstrate the
microphones' contribution to performance on the TragicTalkers dataset, which
offers opportunities to investigate audio-visual approaches in the future.
