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|Title:||Acoustic Event Classification Using Spectrogram Features|
|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.|
|Appears in Collections:||2. Conference Papers|
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