Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/7187
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dc.contributor.authorMulimani, M.
dc.contributor.authorKoolagudi, S.G.
dc.date.accessioned2020-03-30T09:58:36Z-
dc.date.available2020-03-30T09:58:36Z-
dc.date.issued2019
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2018-October, , pp.1460-1464en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7187-
dc.description.abstractThis 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.en_US
dc.titleAcoustic Event Classification Using Spectrogram Featuresen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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