Preprint Article Version 1 This version is not peer-reviewed

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signal to Source Position Coordinates

Version 1 : Received: 29 July 2018 / Approved: 30 July 2018 / Online: 30 July 2018 (09:37:37 CEST)

A peer-reviewed article of this Preprint also exists.

Vera-Diaz, J.M.; Pizarro, D.; Macias-Guarasa, J. Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates. Sensors 2018, 18, 3418. Vera-Diaz, J.M.; Pizarro, D.; Macias-Guarasa, J. Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates. Sensors 2018, 18, 3418.

Journal reference: Sensors 2018, 18, 3418
DOI: 10.3390/s18103418

Abstract

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. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.

Subject Areas

acoustic source localization; microphone arrays; deep learning; convolutional neural networks

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.