About: Early detection of continuous and partial audio events using CNN

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #67448 Early detection of continuous and partial audio events using CNN
sameAs	Early detection of continuous and partial audio events using CNN
http://eprints.org/ontology/hasAccepted	Early detection of continuous and partial audio events using CNN (PDF)
http://eprints.org/ontology/hasDocument	Early detection of continuous and partial audio events using CNN (PDF) Early detection of continuous and partial audio events using CNN (Other) Early detection of continuous and partial audio events using CNN (Other) Early detection of continuous and partial audio events using CNN (Other) Early detection of continuous and partial audio events using CNN (Other) Early detection of continuous and partial audio events using CNN (Other)
dc:hasVersion	Early detection of continuous and partial audio events using CNN (PDF)
Title	Early detection of continuous and partial audio events using CNN
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	2018-09-06
Creator	Ghosh P.K. Narayanan S. Sekhar C.C. Yegnanarayana B. Umesh S. Rao P. Murthy H.A. Prasanna S.R.M. Alku P. Yan Song Huy Phan I. McLoughlin Yue Lang Lam Dang Pham Palaniappan Ramaswamy
status	peer reviewed published
Publisher	International Speech Communication Association
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.
Is Part Of	Proceedings of Interspeech https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/67448#authors
presented at	19th Annual Conference of the International Speech Communication (INTERSPEECH 2018)
volume	2018-S
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 #67448 Early detection of continuous and partial audio events using CNN

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