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Discovering hierarchical speech features using convolutional non-negative matrix factorization

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1 Author(s)
S. Behnke ; Int. Comput. Sci. Inst., Berkeley, CA, USA

Discovering a representation that reflects the structure of a dataset is a first step for many inference and learning methods. This paper aims at finding a hierarchy of localized speech features that can be interpreted as parts. Non-negative matrix factorization (NMF) has been proposed recently for the discovery of parts-based localized additive representations. The author proposes a variant of this method, convolutional NMF, that enforces a particular local connectivity with shared weights. Analysis starts from a spectrogram. The hidden representations produced by convolutional NMF are input to the same analysis method at the next higher level. Repeated application of convolutional NMF yields a sequence of increasingly abstract representations. These speech representations are parts-based, where complex higher-level parts are defined in terms of less complex lower-level ones.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003