Outdoor Acoustic Event Identification using Sound Source Separation and Deep Learning with a Quadrotor-Embedded Microphone Array
-
- 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.
Journal
-
- The Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM
-
The Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (0), 329-330, 2015
The Japan Society of Mechanical Engineers
- Tweet
Details
-
- CRID
- 1390282680876512768
-
- NII Article ID
- 110010043699
-
- ISSN
- 24243116
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- Crossref
- CiNii Articles
-
- Abstract License Flag
- Disallowed