Shishkin, S., Hollosi, D., Doclo, S. et al. (1 more author) (2021) Active learning for sound event classification using Monte-Carlo dropout and PANN embeddings. In: Font, F., Mesaros, A., Ellis, D.P.W., Fonseca, E., Fuentes, M. and Elizalde, B., (eds.) Proceedings of the 6th Workshop on Detection and Classication of Acoustic Scenes and Events (DCASE 2021). 6th Workshop on Detection and Classification of Acoustic Scenes and Events, DCASE 2021, 15-19 Nov 2021, Virtual conference. DCASE Workshop Proceedings . DCASE , pp. 150-154. ISBN 978-84-09-36072-7
Abstract
Labeling audio material to train classifiers comes with a large amount of human labor. In this paper, we propose an active learning method for sound event classification, where a human annotator is asked to manually label sound segments up to a certain labeling budget. The sound event classifier is incrementally re-trained on pseudo-labeled sound segments and manually labeled segments. The segments to be labeled during the active learning process are selected based on the model uncertainty of the classifier, which we propose to estimate using Monte Carlo dropout, a technique for Bayesian inference in neural networks. Evaluation results on the UrbanSound8K dataset show that the proposed active learning method, which uses pre-trained audio neural network (PANN) embeddings as input features, outperforms two baseline methods based on medoid clustering, especially for low labeling budgets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/ |
Keywords: | sound event classification; active learning; Monte Carlo dropout; self-training; transfer learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Oct 2021 10:35 |
Last Modified: | 07 Nov 2022 15:46 |
Status: | Published |
Publisher: | DCASE |
Series Name: | DCASE Workshop Proceedings |
Refereed: | Yes |
Identification Number: | 10.5281/zenodo.5770113 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178511 |