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dc.contributor.authorMulimani, M.
dc.contributor.authorKoolagudi, S.G.
dc.identifier.citationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2018, Vol.2018-September, , pp.3319-3322en_US
dc.description.abstractThis paper presents a novel Bag-of-Visual-Words (BoVW) approach, to represent the grayscale spectrograms of acoustic events. Such, BoVW representations are referred as histograms of visual features, used for Acoustic Event Classification (AEC). Further, Chi-square distance between histograms of visual features evaluated, which generates kernel to Support Vector Machines (Chi-square SVM) classifier. Evaluation of the proposed histograms of visual features together with Chi-square SVM classifier is conducted on different categories of acoustic events from UPC-TALP corpora in clean and different noise conditions. Results show that proposed approach is more robust to noise and achieves improved recognition accuracy compared to other methods. � 2018 International Speech Communication Association. All rights reserved.en_US
dc.titleRobust acoustic event classification using bag-of-visual-wordsen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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