Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Zhang, Liang, Towsey, Michael, Xie, Jie, Zhang, Jinglan, & Roe, Paul (2016) Using multi-label classification for acoustic pattern detection and assisting bird species surveys. Applied Acoustics, 110, pp. 91-98.

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Abstract

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

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ID Code: 94575
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Soundscape ecology, Computational ecology, Acoustic indices, Multi-label classification
DOI: 10.1016/j.apacoust.2016.03.027
ISSN: 0003-682X
Subjects: Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ECOLOGICAL APPLICATIONS (050100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Decision Support and Group Support Systems (080605)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Facilities: Science and Engineering Centre
Deposited On: 07 Apr 2016 23:58
Last Modified: 02 May 2016 17:21

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