About: Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #73780 Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
http://eprints.org/ontology/hasAccepted	Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (PDF)
http://eprints.org/ontology/hasDocument	Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (PDF) Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (Other) Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (Other) Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (Other) Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (Other) Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (Other)
dc:hasVersion	Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene? (PDF)
Title	Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-06-08
Creator	Philipp Koch Huy Phan I. McLoughlin Lam Dang Pham Maarten De Vos Afred Mertins Oliver Y. Chén
status	non peer reviewed published
Publisher	The Audio Engineering Society
abstract	Due to the variability in characteristics of audio scenes, some scenes can naturally be recognized earlier than others. In this work, rather than using equal-length snippets for all scene categories, as is common in the literature, we study to which temporal extent an audio scene can be reliably recognized given state-of-the-art models. Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task. To achieve these goals, we employ two single-network systems relying on a convolutional neural network and a recurrent neural network for classification as well as early fusion and late fusion of these networks. Experimental results on the LITIS-Rouen dataset show that some scenes can be reliably recognized with a few seconds while other scenes require significantly longer durations. In addition, model fusion is shown to be the most beneficial when the signal length is short.
Is Part Of	AES E-LIBRARY https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/73780#authors
presented at	2019 AES Conference on Audio Forensics
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #73780 Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?

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