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Learning nonnegative features of spectro-temporal sounds for classification yong-choon cho digital media r&d center.
| Content Provider | CiteSeerX |
|---|---|
| Author | Korea, Samsung |
| Abstract | 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. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Nonnegative Matrix Factorization Independent Component Analysis Spectrotemporal Sound Holistic Feature General Sound Classification Sound Classification Parts-based Representation Useful Behavior New Way Spectro-temporal Sound Nonnegative Feature |
| Content Type | Text |
| Resource Type | Article |
