Outdoor Acoustic Event Identification using Sound Source Separation and Deep Learning with a Quadrotor-Embedded Microphone Array
Satoshi Uemura, Osamu Sugiyama, Ryosuke Kojima, Kazuhiro Nakadai · The Abstracts of the international conference on advanced mechatronics toward evolutionary fusion of IT and mechatronics ICAM · 2015
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.