HARK-Bird-Box: A Portable Real-time Bird Song Scene Analysis System
Ryosuke Kojima, Osamu Sugiyama, Kotaro Hoshiba, Reiji Suzuki, Kazuhiro Nakadai
Abstract
This paper addresses real-time bird song scene analysis. Observation of animal behavior such as communication of wild birds would be aided by a portable device implementing a real-time system that can localize sound sources, measure their timing, classify their sources, and visualize these factors of sources. The difficulty of such a system is an integration of these functions considering the real-time requirement. To realize such a system, we propose a cascaded approach, cascading sound source detection, localization, separation, feature extraction, classification, and visualization for bird song analysis. Our system is constructed by combining an open source software for robot audition called HARK and a deep learning library to implement a bird song classifier based on a convolutional neural network (CNN). Considering portability, we implemented this system on a single-board computer, Jetson TX2, with a microphone array and developed a prototype device for bird song scene analysis. A preliminary experiment confirms a computational time for the whole system to realize a real-time system. Also, an additional experiment with a bird song dataset revealed a trade-off relationship between classification accuracy and time consuming and the effectiveness of our classifier.
BibTeX
@inproceedings{iros2018_harkbirdboxaport,
title = {HARK-Bird-Box: A Portable Real-time Bird Song Scene Analysis System},
author = {Ryosuke Kojima and Osamu Sugiyama and Kotaro Hoshiba and Reiji Suzuki and Kazuhiro Nakadai},
booktitle = {IROS 2018},
year = {2018}
}