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https://idr.l2.nitk.ac.in/jspui/handle/123456789/7187
Title: | Acoustic Event Classification Using Spectrogram Features |
Authors: | Mulimani, M. Koolagudi, S.G. |
Issue Date: | 2019 |
Citation: | IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2018-October, , pp.1460-1464 |
Abstract: | 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. |
URI: | http://idr.nitk.ac.in/jspui/handle/123456789/7187 |
Appears in Collections: | 2. Conference Papers |
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