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JRM Vol.29 No.1 pp. 37-48
doi: 10.20965/jrm.2017.p0037
(2017)

Paper:

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Sound Source Localization Using Deep Learning Models

Nelson Yalta*, Kazuhiro Nakadai**, and Tetsuya Ogata*

*Intermedia Art and Science Department, Waseda University
3-4-1 Ohkubo, Shinjuku, Tokyo 169-8555, Japan

**Honda Research Institute Japan Co., Ltd.
8-1 Honcho, Wako, Saitama 351-0188, Japan

Received:
July 22, 2016
Accepted:
December 28, 2016
Published:
February 20, 2017
Creative Commons License
Keywords:
sound source localization, deep learning, deep residual networks
Abstract
This study proposes the use of a deep neural network to localize a sound source using an array of microphones in a reverberant environment. During the last few years, applications based on deep neural networks have performed various tasks such as image classification or speech recognition to levels that exceed even human capabilities. In our study, we employ deep residual networks, which have recently shown remarkable performance in image classification tasks even when the training period is shorter than that of other models. Deep residual networks are used to process audio input similar to multiple signal classification (MUSIC) methods. We show that with end-to-end training and generic preprocessing, the performance of deep residual networks not only surpasses the block level accuracy of linear models on nearly clean environments but also shows robustness to challenging conditions by exploiting the time delay on power information.
Using a deep learning model, the robot locate the sound source from a multiple channel audio stream input

Using a deep learning model, the robot locate the sound source from a multiple channel audio stream input

Cite this article as:
N. Yalta, K. Nakadai, and T. Ogata, “Sound Source Localization Using Deep Learning Models,” J. Robot. Mechatron., Vol.29 No.1, pp. 37-48, 2017.
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