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JRM Vol.33 No.3 pp. 515-525
doi: 10.20965/jrm.2021.p0515
(2021)

Paper:

Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks

Kazuki Fujimori*, Bisser Raytchev*, Kazufumi Kaneda*, Yasufumi Yamada*, Yu Teshima**, Emyo Fujioka**, Shizuko Hiryu**, and Toru Tamaki***

*Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan

**Doshisha University
1-3 Tatara-miyakodani, Kyotanabe, Kyoto 610-0394, Japan

***Nagoya Institute of Technology
Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan

Received:
December 18, 2020
Accepted:
April 28, 2021
Published:
June 20, 2021
Keywords:
bat, multichannel, ultrasound signal, CNN, location estimation
Abstract
Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks

Bat localization with deep learning

We propose a method that uses ultrasound audio signals from a multichannel microphone array to estimate the positions of flying bats. The proposed model uses a deep convolutional neural network that takes multichannel signals as input and outputs the probability maps of the locations of bats. We present experimental results using two ultrasound audio clips of different bat species and show numerical simulations with synthetically generated sounds.

Cite this article as:
Kazuki Fujimori, Bisser Raytchev, Kazufumi Kaneda, Yasufumi Yamada, Yu Teshima, Emyo Fujioka, Shizuko Hiryu, and Toru Tamaki, “Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks,” J. Robot. Mechatron., Vol.33, No.3, pp. 515-525, 2021.
Data files:
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Last updated on Aug. 03, 2021