Outdoor Acoustic Event Identification using Sound Source Separation and Deep Learning with a Quadrotor-Embedded Microphone Array

DOI Web Site 3 Citations
  • Uemura Satoshi
    Graduate School of Information Science and Engineering, Tokyo Institute of Technology
  • Sugiyama Osamu
    Graduate School of Information Science and Engineering, Tokyo Institute of Technology
  • Kojima Ryosuke
    Graduate School of Information Science and Engineering, Tokyo Institute of Technology
  • Nakadai Kazuhiro
    Graduate School of Information Science and Engineering, Tokyo Institute of Technology:Honda Research Institute Japan Co., Ltd.

Abstract

We present acoustic event identification by integration of sound source separation and deep learning based on a convolutional neural network for extremely noisy acoustics signals captured with a 16 ch microphone array embedded in an Unmanned Aerial Vehicle (UAV).We showed that the proposed method can identify over 98% sound sources correctly for a 10 class classification task using 16 ch recorded sound data with a microphone array embedded in a quadrotor.

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