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Bioacoustic detection with wavelet-conditioned convolutional neural networks

Abstract:

Many real-world time series analysis problems are characterized by low signal-to-noise ratios and compounded by scarce data. Solutions to these types of problems often rely on handcrafted features extracted in the time or frequency domain. Recent high-profile advances in deep learning have improved performance across many application domains; however, they typically rely on large data sets that may not always be available. This paper presents an application of deep learning for acoustic event...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s00521-018-3626-7

Authors


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Institution:
University of Oxford
Department:
Department of Engineering Science
Role:
Author
ORCID:
0000-0002-2551-840X
More by this author
Institution:
University of Oxford
Department:
Zoology
Role:
Author
Publisher:
Springer
Journal:
Neural Computing and Applications More from this journal
Volume:
32
Issue:
4
Pages:
915-927
Publication date:
2018-08-01
Acceptance date:
2018-07-11
DOI:
EISSN:
1433-3058
ISSN:
0941-0643
Language:
English
Keywords:
Pubs id:
pubs:907514
UUID:
uuid:1c899347-7047-41d7-8fcf-4548d311eb92
Local pid:
pubs:907514
Source identifiers:
907514
Deposit date:
2019-01-29

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