Sound source localization using deep learning models

Nelson Yalta, Kazuhiro Nakadai, Tetsuya Ogata

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)37-48
Number of pages12
JournalJournal of Robotics and Mechatronics
Volume29
Issue number1
DOIs
Publication statusPublished - 2017 Feb

Keywords

  • Deep learning
  • Deep residual networks
  • Sound source localization

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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