Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Robust acoustic event classification using bag-of-visual-words(International Speech Communication Association publication@isca-speech.org 4 Rue des Fauvettes - Lous Tourils Baixas 66390, 2018) Mulimani, M.; Koolagudi, S.G.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. © 2018 International Speech Communication Association. All rights reserved.Item Acoustic Event Classification Using Spectrogram Features(Institute of Electrical and Electronics Engineers Inc., 2018) Mulimani, M.; Koolagudi, S.G.This paper investigates a new feature extraction method to extract different features from the spectrogram of an audio signal for Acoustic Event Classification (AEC). A new set of features is formulated and extracted from local spectrogram regions named blocks. The average recognition performance of proposed spectrogram based features and Mel-frequency cepstral coefficients (MFCCs) with their deltas and accelerations on Support Vector Machines (SVM) is compared. In this work, different categories of acoustic events are considered from the Freiburg-106 dataset. Proposed features show significantly improved performance over conventional Mel-frequency cepstral coefficients (MFCCs) for Acoustic Event Classification. © 2018 IEEE.Item Locality-constrained linear coding based fused visual features for robust acoustic event classification(International Speech Communication Association, 2019) Mulimani, M.; Koolagudi, G.K.In this paper, a novel Fused Visual Features (FVFs) are proposed for Acoustic Event Classification (AEC) in the meeting room and office environments. The codes of Visual Features (VFs) are evaluated from row vectors and Scale Invariant Feature Transform (SIFT) vectors of the grayscale Gammatonegram of an acoustic event separately using Locality-constrained Linear Coding (LLC). Further, VFs from row vectors and SIFT vectors of the grayscale Gammatonegram are fused to get FVFs. Performance of the proposed FVFs is evaluated on acoustic events of publicly available UPC-TALP and DCASE datasets in clean and noisy conditions. Results show that proposed FVFs are robust to noise and achieve overall recognition accuracy of 96.40% and 90.45% on UPC-TALP and DCASE datasets, respectively. © 2019 ISCA