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DC Field | Value | Language |
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dc.contributor.author | Mulimani, M. | |
dc.contributor.author | Koolagudi, S.G. | |
dc.date.accessioned | 2020-03-30T09:58:36Z | - |
dc.date.available | 2020-03-30T09:58:36Z | - |
dc.date.issued | 2019 | |
dc.identifier.citation | IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2018-October, , pp.1460-1464 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/7187 | - |
dc.description.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. | en_US |
dc.title | Acoustic Event Classification Using Spectrogram Features | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | 2. Conference Papers |
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