About: A robust framework for acoustic scene classification

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91418 A robust framework for acoustic scene classification
Title	A robust framework for acoustic scene classification
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	2019-09-20
Creator	Huy Phan I. McLoughlin Lam Dang Pham Palaniappan Ramaswamy
status	peer reviewed published
abstract	Acoustic scene classification (ASC) using front-end timefrequency features and back-end neural network classifiers has demonstrated good performance in recent years. However a profusion of systems has arisen to suit different tasks and datasets, utilising different feature and classifier types. This paper aims at a robust framework that can explore and utilise a range of different time-frequency features and neural networks, either singly or merged, to achieve good classification performance. In particular, we exploit three different types of frontend time-frequency feature; log energy Mel filter, Gammatone filter and constant Q transform. At the back-end we evaluate effective a two-stage model that exploits a Convolutional Neural Network for pre-trained feature extraction, followed by Deep Neural Network classifiers as a post-trained feature adaptation model and classifier. We also explore the use of a data augmentation technique for these features that effectively generates a variety of intermediate data, reinforcing model learning abilities, particularly for marginal cases. We assess performance on the DCASE2016 dataset, demonstrating good classification accuracies exceeding 90%, significantly outperforming the DCASE2016 baseline and highly competitive compared to state-of-the-art systems.
Is Part Of	https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/91418#authors
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 #91418 A robust framework for acoustic scene classification

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