Faculty Publications
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Publications by NITK Faculty
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Item Robust Acoustic Event Classification using Fusion Fisher Vector features(Elsevier Ltd, 2019) Mulimani, M.; Koolagudi, S.G.In this paper, a novel Fusion Fisher Vector (FFV) features are proposed for Acoustic Event Classification (AEC) in the meeting room environments. The monochrome images of a pseudo-color spectrogram of an acoustic event are represented as Fisher vectors. First, irrelevant feature dimensions of each Fisher vector are discarded using Principal Component Analysis (PCA) and then, resulting Fisher vectors are fused to get FFV features. Performance of the FFV features is evaluated on acoustic events of UPC-TALP dataset in clean and different noisy conditions. Results show that proposed FFV features are robust to noise and achieve overall 94.32% recognition accuracy in clean and different noisy conditions. © 2019 Elsevier LtdItem Acoustic scene classification using projection Kervolutional neural network(Springer, 2023) Mulimani, M.; Nandi, R.; Koolagudi, S.G.In this paper, a novel Projection Kervolutional Neural Network (ProKNN) is proposed for Acoustic Scene Classification (ASC). ProKNN is a combination of two special filters known as the left and right projection layers and Kervolutional Neural Network (KNN). KNN replaces the linearity of the Convolutional Neural Network (CNN) with a non-linear polynomial kernel. We extend the ProKNN to learn from the features of two channels of audio recordings in the initial stage. The performance of the ProKNN is evaluated on the two publicly available datasets: TUT Urban Acoustic Scenes 2018 and TUT Urban Acoustic Scenes Mobile 2018 development datasets. Results show that the proposed ProKNN outperforms the existing systems with an absolute improvement of accuracy of 8% and 14% on TUT Urban Acoustic Scenes 2018 and TUT Urban Acoustic Scenes Mobile 2018 development datasets respectively, as compared to the baseline model of Detection and Classification of Acoustic Scene and Events (DCASE) - 2018 challenge. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Acoustic Scene Classification using Deep Fisher network(Elsevier Inc., 2023) Venkatesh, S.; Mulimani, M.; Koolagudi, S.G.Acoustic Scene Classification (ASC) is the task of assigning a semantic label to an audio recording, based on the surrounding environment. In this work, a Fisher network is introduced for ASC. The proposed method mimics the working mechanism of a feed-forward Convolutional Neural Network (CNN) where, output of a layer is fed as an input to the succeeding layer. The Fisher network consists of a feature extraction step followed by a Fisher layer. The Fisher layer has three sub-layers, namely, Fisher Vector (FV) encoder, temporal pyramid and normalization layers along with feature reduction layer. Gammatone Time Cepstral Coefficients (GTCCs) and Mel-spectrograms are the features encoded as Fisher vector representation in FV encoder sub-layer. Temporal information of the Fisher vectors is retained using temporal pyramid sub-layer. After temporal pyramids are extracted from Fisher vectors, they are available as a feature vector. Irrelevant dimensions of the temporal pyramids are reduced further using Principal Component Analysis (PCA) in normalization and PCA sub-layers. The proposed model is evaluated on five DCASE datasets, TUT Urban Acoustic Scenes 2018 and Mobile, DCASE 2019 Acoustic Scene Classification Task 1(a) and Task 1(b), TAU Urban Acoustic Scenes 2020 datasets. The overall classification accuracy is 93%, 91%, 92%, 91% and 89% for TUT 2018, TUT Mobile 2018, DCASE Task 1(a) 2019, DCASE Task 1(b) 2019, and TAU Urban Acoustic Scenes 2020 datasets, respectively. The proposed model performed much better than the state-of-the-art ASC systems. © 2023 Elsevier Inc.