Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

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This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN).

• The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features.
• Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps.
• We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones.