Learning nonnegative features of spectro-temporal sounds for classification
نویسندگان
چکیده
In this paper we present a method of sound classification which exploits a parts-based representation of spectrotemporal sounds, employing the nonnegative matrix factorization (NMF) [1]. We illustrate a new way of learning nonnegative features using a variant of NMF and show its useful behavior in the task of general sound classification with comparison to independent component analysis (ICA) which produces holistic features.
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