Non-negative component parts of sound for classification

Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditory processing and to the task of sound classification. In contrast, parts-based representation is an alternative way of understanding object recognition in brain. In this paper we employ the non-negative matrix factorization (NMF) [D.D. Lee et al., 1999] which learns parts-based representation in the task of sound classification. Methods of feature extraction from spectro-temporal sounds using the NMF in the absence or presence of noise are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.