Robust features for environmental sound classification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

Robust features for environmental sound classification


In this paper we describe algorithms to classify environmental sounds with the aim of providing contextual information to devices such as hearing aids for optimum performance. We use signal sub-band energy to construct signal-dependent dictionary and matching pursuit algorithms to obtain a sparse representation of a signal. The coefficients of the sparse vector are used as weights to compute weighted features. These features, along with mel frequency cepstral coefficients (MFCC) are used as feature vectors for classification. Experimental results show that the proposed method gives a maximum accuracy of 95.6 % while classifying 14 categories of environmental sound using a gaussian mixture model (GMM).
Fichier principal
Vignette du fichier
toConf.pdf (435.85 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01456201 , version 1 (04-02-2017)



Sunit Sivasankaran, K.M.M Prabhu. Robust features for environmental sound classification. 2013 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Jan 2013, Bangalore, India. pp.1 - 6, ⟨10.1109/CONECCT.2013.6469297⟩. ⟨hal-01456201⟩
60 View
564 Download



Gmail Facebook X LinkedIn More