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research
Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News
Authors
Alexandros Lazaridis
Casey M.
+7Β more
Dempster A. P.
Eyben F.
Iosif Mporas
Nikos Fakotakis
Perperis T.
Theodoros Theodorou
Wollmer M.
Publication date
23 December 2016
Publisher
'World Scientific Pub Co Pte Lt'
Doi
Cite
Abstract
This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. T. Theodorou, I. Mpoas, A. Lazaridis, N. Fakotakis, 'Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News', International Journal on Artificial Intelligence Tools, Vol. 26 (2), April 2017, 1750005 (13 pages), DOI: 10.1142/S021821301750005. Β© The Author(s).In this paper we describe an automatic sound recognition scheme for radio broadcast news based on principal component clustering with respect to the discrimination ability of the principal components. Specifically, streams of broadcast news transmissions, labeled based on the audio event, are decomposed using a large set of audio descriptors and project into the principal component space. A data-driven algorithm clusters the relevance of the components. The component subspaces are used by sound type classifier. This methodology showed that the k-nearest neighbor and the artificial intelligent network provide good results. Also, this methodology showed that discarding unnecessary dimension works in favor on the outcome, as it hardly deteriorates the effectiveness of the algorithms.Peer reviewe
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University of Hertfordshire Research Archive
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Last time updated on 17/02/2017
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info:doi/10.1142%2Fs0218213017...
Last time updated on 05/06/2019