Outdoor Acoustic Event Identification with DNN Using a Quadrotor-Embedded Microphone Array

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  • Sugiyama Osamu
    Preemptive Medicine & Lifestyle-Related Disease Research Center, Kyoto University Hospital
  • Uemura Satoshi
    Graduate School of Information Science and Engineering, Tokyo Institute of Technology
  • Nagamine Akihide
    Department of Electrical and Electronic Engineering, School of Engineering, Tokyo Institute of Technology
  • Kojima Ryosuke
    Graduate School of Information Science and Engineering, Tokyo Institute of Technology
  • Nakamura Keisuke
    Honda Research Institute Japan Co., Ltd.
  • Nakadai Kazuhiro
    Graduate School of Information Science and Engineering, Tokyo Institute of Technology Honda Research Institute Japan Co., Ltd.

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<p>This paper addresses Acoustic Event Identification (AEI) of acoustic signals observed with a microphone array embedded in a quadrotor that is flying in a noisy outdoor environment. In such an environment, noise generated by rotors, wind, and other sound sources is a big problem. To solve this, we propose the use of a combination of two approaches that have recently been introduced: Sound Source Separation (SSS) and Sound Source Identification (SSI). SSS improves the Signal-to-Noise Ratio (SNR) of the input sound, and SSI is then performed on the SNR-improved sound. Two SSS methods are investigated. One is a single channel algorithm, Robust Principal Component Analysis (RPCA), and the other is Geometric High-order Decorrelation-based Source Separation (GHDSS-AS), known as a multichannel method. For SSI, we investigate two types of deep neural networks namely Stacked denoising Autoencoder (SdA) and Convolutional Neural Network (CNN), which have been extensively studied as highly-performant approaches in the fields of automatic speech recognition and visual object recognition. Preliminary experiments have showed the effectiveness of the proposed approaches, a combination of GHDSS-AS and CNN in particular. This combination correctly identified over 80% of sounds in an 8-class sound classification recorded by a hovering quadrotor. In addition, the CNN identifier that was implemented could be handled even with a low-end CPU by measuring the prediction time.</p>

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