Analyzing big environmental audio with frequency preserving autoencoders
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105816771. |
Description
Continuous audio recordings are playing an ever more important role in conservation and biodiversity monitoring, however, listening to these recordings is often infeasible, as they can be thousands of hours long. Automating analysis using machine learning is in high demand. However, these algorithms require a feature representation. Several methods for generating feature representations for these data have been developed, using techniques such as domain-specific features and deep learning. However, domain-specific features are unlikely to be an ideal representation of the data and deep learning methods often require extensively labeled data.In this paper, we propose a method for generating a frequency-preserving autoencoder-based feature representation for unlabeled ecological audio. We evaluate multiple frequency-preserving autoencoder-based feature representations using a hierarchical clustering sample task. We compare this to a basic autoencoder feature representation, MFCC, and spectral acoustic indices. Experimental results show that some of these non-square autoencoder architectures compare well to these existing feature representations.This novel method for generating a feature representation for unlabeled ecological audio will offer a fast, general way for ecologists to generate a feature representation of their audio, which does not require extensively labeled data.
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ID Code: | 228402 | ||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
Series Name: | Proceedings - IEEE 17th International Conference on eScience, eScience 2021 | ||||
ORCID iD: |
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Measurements or Duration: | 10 pages | ||||
Keywords: | Autoencoder, Deep Learning, Ecoacoustics, Machine Learning | ||||
DOI: | 10.1109/eScience51609.2021.00017 | ||||
ISBN: | 978-1-6654-4708-9 | ||||
Pure ID: | 105816771 | ||||
Divisions: | Current > Research Centres > Centre for Data Science Current > Research Centres > Centre for the Environment Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science |
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Copyright Owner: | 2021 IEEE | ||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||
Deposited On: | 17 Feb 2022 02:41 | ||||
Last Modified: | 01 Mar 2024 00:17 |
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