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dc.contributor.authorMulimani, M.
dc.contributor.authorKoolagudi, S.G.
dc.date.accessioned2020-03-31T08:42:12Z-
dc.date.available2020-03-31T08:42:12Z-
dc.date.issued2019
dc.identifier.citationApplied Acoustics, 2019, Vol.155, , pp.130-138en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/12820-
dc.description.abstractIn 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 Ltden_US
dc.titleRobust Acoustic Event Classification using Fusion Fisher Vector featuresen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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