About: Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #85290 Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework
sameAs	Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework
http://eprints.org/ontology/hasAccepted	Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (PDF)
http://eprints.org/ontology/hasDocument	Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (PDF) Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (Other) Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (Other) Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (Other) Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (Other) Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (Other)
dc:hasVersion	Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework (PDF)
Title	Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework
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	2020-12-19
Creator	Truc Nguyen Huy Phan I. McLoughlin Lam Dang Pham Afred Mertins Palaniappan Ramaswamy
status	peer reviewed published
Publisher	Elsevier
abstract	This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The high-level features and their combination (via a trained feature combiner) are then fed into different decoder models comprising random forest regression, DNNs and a mixture of experts, for back-end classification. We conduct extensive experiments to evaluate the performance of this framework on various ASC datasets, including LITIS Rouen and IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Task 1, 2017 Task 1, 2018 Tasks 1A & 1B and 2019 Tasks 1A & 1B. The experimental results highlight two main contributions; the first is an effective method for high-level feature extraction from multi-spectrogram input via the novel CNN-DNN architecture encoder network, and the second is the proposed decoder which enables the framework to achieve competitive results on various datasets. The fact that a single framework is highly competitive for several different challenges is an indicator of its robustness for performing general ASC tasks.
Is Part Of	Digital Signal Processing https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/85290#authors
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 #85290 Robust acoustic scene classification using a multi-spectrogram encoder-decoder framework

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