About: Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #75276 Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification
sameAs	Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification
http://eprints.org/ontology/hasDocument	Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (PDF) Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (Other) Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (Other) Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (Other) Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (Other) Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (Other)
http://eprints.org/ontology/hasPublished	Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (PDF)
dc:hasVersion	Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification (PDF)
Title	Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020
Creator	Yan Song Huy Phan I. McLoughlin Palaniappan Ramaswamy Zhi-Peng Xie
status	peer reviewed published
Publisher	Springer
abstract	This paper explores the use of three different two-dimensional time-frequency features for audio event classification with deep neural network back-end classifiers. The evaluations use spectrogram, cochleogram and constant-Q transform based images for classification of 50 classes of audio events in varying levels of acoustic background noise, revealing interesting performance patterns with respect to noise level, feature image type and classifier. Evidence is obtained that two well-performing features, the spectrogram and cochleogram, make use of information that is potentially complementary in the input features. Feature fusion is thus explored for each pair of features, as well as for all tested features. Results indicate that a fusion of spectrogram and cochleogram information is particularly beneficial, yielding an impressive 50-class accuracy of over 96% in 0dB SNR, and exceeding 99% accuracy in 10dB SNR and above. Meanwhile the cochleogram image feature is found to perform well in extreme noise cases of -5dB and -10dB SNR.
Is Part Of	Circuits, Systems, and Signal Processing https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/75276#authors
volume	39
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #75276 Time-Frequency Feature Fusion for Noise-Robust Audio Event Classification

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