Mulimani, M.Koolagudi, S.G.2026-02-062018Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2018, Vol.2018-September, , p. 3319-33222308457Xhttps://doi.org/10.21437/Interspeech.2018-1905https://idr.nitk.ac.in/handle/123456789/31628This 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.Acoustic Event Classification (AEC)Bag-of-Visual-Words (BoVW)Chi-square kernel SVMHistograms of visual features