Robust Acoustic Event Classification Using Bag-of-Visual-Words

Manjunath Mulimani and Shashidhar G Koolagudi

Abstract:

This 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.

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Cite as: Mulimani, M., Koolagudi, S.G. (2018) Robust Acoustic Event Classification Using Bag-of-Visual-Words. Proc. Interspeech 2018, 3319-3322, DOI: 10.21437/Interspeech.2018-1905.

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BiBTeX Entry:

@inproceedings{Mulimani2018,
author={Manjunath Mulimani and Shashidhar G Koolagudi},
title={Robust Acoustic Event Classification Using Bag-of-Visual-Words},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={3319--3322},
doi={10.21437/Interspeech.2018-1905},
url={http://dx.doi.org/10.21437/Interspeech.2018-1905} }