Robust Acoustic Event Classification using Fusion Fisher Vector features

Manjunath Mulimani, Shashidhar G. Koolagudi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

In 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.

Original languageEnglish
Pages (from-to)130-138
Number of pages9
JournalApplied Acoustics
Volume155
DOIs
Publication statusPublished - 01-12-2019

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics

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