Localizing Bird Songs Using an Open Source Robot Audition System with a Microphone Array

Reiji Suzuki, Shiho Matsubayashi, Kazuhiro Nakadai, Hiroshi G. Okuno


Auditory scene analysis is critical in observing bio-diversity and understanding social behavior of animals in natural habitats because many animals and birds sing or call and environmental sounds are made. To understand acoustic interactions among songbirds, we need to collect spatiotemporal data for a long period of time during which multiple individuals and species are singing simultaneously. We are developing HARKBird, which is an easily-available and portable system to record, localize, and analyze bird songs. It is composed of a laptop PC with an open source robot audition system HARK (Honda Research Institute Japan Audition for Robots with Kyoto University) and a commercially available low-cost microphone array. HARKBird helps us annotate bird songs and grasp the soundscape around the microphone array by providing the direction of arrival (DOA) of each localized source and its separated sound automatically. In this paper, we briefly introduce our system and show an example analysis of a track recorded at the experimental forest of Nagoya University, in central Japan. We demonstrate that HARKBird can extract birdsongs successfully by combining multiple localization results with appropriate parameter settings that took account of ecological properties of environment around a microphone array and species-specific properties of bird songs.


DOI: 10.21437/Interspeech.2016-782

Cite as

Suzuki, R., Matsubayashi, S., Nakadai, K., Okuno, H.G. (2016) Localizing Bird Songs Using an Open Source Robot Audition System with a Microphone Array. Proc. Interspeech 2016, 2626-2630.

Bibtex
@inproceedings{Suzuki+2016,
author={Reiji Suzuki and Shiho Matsubayashi and Kazuhiro Nakadai and Hiroshi G. Okuno},
title={Localizing Bird Songs Using an Open Source Robot Audition System with a Microphone Array},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-782},
url={http://dx.doi.org/10.21437/Interspeech.2016-782},
pages={2626--2630}
}