AI & Machine Learning

In Proc. of the IEEE International Conference on Audio and Speech Signal Processing (ICASSP)

Sparse and Shift-invariant Feature Extraction from Non- negative Data

Paris Smaragdis, B. Raj, M. Shashanka

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In this paper we describe a technique that allows the extraction of multiple local shift-invariant features from analysis of non-negative data of arbitrary dimensionality. Our approach employs a probabilis- tic latent variable model with sparsity constraints. We demonstrate its utility by performing feature extraction in a variety of domains ranging from audio to images and video.