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Robust features for environmental sound classification

Abstract : 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).
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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⟩



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