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This study considers the problem of detecting and locating an active talker'shorizontal position from multichannel audio captured by a microphone array. Werefer to this as active speaker detection and localization (ASDL). Our goal wasto investigate the performance of spatial acoustic features extracted from themultichannel audio as the input of a convolutional recurrent neural network(CRNN), in relation to the number of channels employed and additive noise. Tothis end, experiments were conducted to compare the generalizedcross-correlation with phase transform (GCC-PHAT), the spatial cue-augmentedlog-spectrogram (SALSA) features, and a recently-proposed beamforming method,evaluating their robustness to various noise intensities. The array apertureand sampling density were tested by taking subsets from the 16-microphonearray. Results and tests of statistical significance demonstrate themicrophones' contribution to performance on the TragicTalkers dataset, whichoffers opportunities to investigate audio-visual approaches in the future.

Audio Inputs for Active Speaker Detection and Localization via Microphone Array