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Early detection of continuous and partial audio events using CNN

McLoughlin, Ian Vince, Song, Yan, Pham, Lam Dang, Palaniappan, Ramaswamy, Phan, Huy, Lang, Yue (2018) Early detection of continuous and partial audio events using CNN. In: Proceedings of Interspeech. 2018-S. pp. 3314-3318. International Speech Communication Association (doi:10.21437/Interspeech.2018-1821)

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http://dx.doi.org/10.21437/Interspeech.2018-1821

Abstract

Sound event detection is an extension of the static auditory classification task into continuous environments, where performance depends jointly upon the detection of overlapping events and their correct classification. Several approaches have been published to date which either develop novel classifiers or employ well-trained static classifiers with a detection front-end. This paper takes the latter approach, by combining a proven CNN classifier acting on spectrogram image features, with time-frequency shaped energy detection that identifies seed regions within the spectrogram that are characteristic of auditory energy events. Furthermore, the shape detector is optimised to allow early detection of events as they are developing. Since some sound events naturally have longer durations than others, waiting until completion of entire events before classification may not be practical in a deployed system. The early detection capability of the system is thus evaluated for the classification of partial events. Performance for continuous event detection is shown to be good, with accuracy being maintained well when detecting partial events.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.21437/Interspeech.2018-1821
Uncontrolled keywords: Audio classification, Convolutional neural networks, Segmentation, Sound event detection, Image segmentation, Neural networks, Spectrographs, Speech communication, Audio classification, Classification tasks, Convolutional neural network, Deployed systems, Detection capability, Energy detection, Sound event detection, Static classifier, Audio acoustics
Subjects: T Technology
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Ian McLoughlin
Date Deposited: 29 Jun 2018 09:16 UTC
Last Modified: 09 Jul 2019 09:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67448 (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince: https://orcid.org/0000-0001-7111-2008
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
Phan, Huy: https://orcid.org/0000-0003-4096-785X
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