Sankupellay, Mangalam, and Konovalov, Dmitry (2018) Bird call recognition using deep convolutional neural network, ResNet-50. In: Proceedings of the Australian Acoustical Society Conference. 134. From: AAS2018: Acoustics 2018: hear to listen, 6-9 November 2018, Adelaide, SA, Australia.

PDF (Published version) - Published Version Restricted to Repository staff only

Abstract

Birds are an important group of animal that ecologist monitor using autonomous recordings units as an crucial indicator of health of an environment. There is not yet an adequate method for automated bird call recognition in acoustic recordings due to high variations in bird calls and the challenges associated with bird call recognition. In this paper, we use ResNet-50, a deep convolutional neural network architecture for automated bird call recogni- tion. We used a publicly available dataset consisting of calls from 46 different bird species. Spectrograms (visual features) extracted from the bird calls were used as input for ResNet-50. We were able to achieve 60%-72% accuracy of bird call recognition using ResNet-50.

Item ID:	57213
Item Type:	Conference Item (Research - E1)
Date Deposited:	26 Feb 2019 23:42
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 100%
SEO Codes:	96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960899 Flora, Fauna and Biodiversity of Environments not elsewhere classified @ 100%
Downloads:	Total: 1
	More Statistics