About: Enabling Early Audio Event Detection With Neural Networks

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #67162 Enabling Early Audio Event Detection With Neural Networks
sameAs	Enabling Early Audio Event Detection With Neural Networks
http://eprints.org/ontology/hasAccepted	Enabling Early Audio Event Detection With Neural Networks (PDF)
http://eprints.org/ontology/hasDocument	Enabling Early Audio Event Detection With Neural Networks (PDF) Enabling Early Audio Event Detection With Neural Networks (Other) Enabling Early Audio Event Detection With Neural Networks (Other) Enabling Early Audio Event Detection With Neural Networks (Other) Enabling Early Audio Event Detection With Neural Networks (Other) Enabling Early Audio Event Detection With Neural Networks (Other)
dc:hasVersion	Enabling Early Audio Event Detection With Neural Networks (PDF)
Title	Enabling Early Audio Event Detection With Neural Networks
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2018-09-13
Creator	Philipp Koch A. Mertins Huy Phan I. McLoughlin
status	peer reviewed published
Publisher	IEEE
abstract	This paper presents a methodology for early detection of audio events from audio streams. Early detection is the ability to infer an ongoing event during its initial stage. The proposed system consists of a novel inference step coupled with dual parallel tailored-loss deep neural networks (DNNs). The DNNs share a similar architecture except for their loss functions, i.e. weighted loss and multitask loss, which are designed to efficiently cope with issues common to audio event detection. The inference step is newly introduced to make use of the network output for recognizing ongoing events. The monotonicity of the detection function is required for reliable early detection, and will also be proved. Experiments on the ITC-Irst database show that the proposed system achieves state-of-the-art detection performance. Furthermore, even partial events are sufficient to achieve good performance similar to that obtained when an entire event is observed, enabling early event detection.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/67162#authors
presented at	2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #67162 Enabling Early Audio Event Detection With Neural Networks

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