Ecole d'ingénieur et centre de recherche en télécommunications

Seminar: Online Pattern Learning for Non-Negative Convolutive Sparse Coding

Ravichander Vipperla - Post doctoral researcher, MM department

Communications Multimédia

Date: 26 août 2011

Lieu: Eurecom - salle EN05

The unsupervised learning of spectro-temporal speech patterns is relevant in a broad range of tasks. Convolutive non-negative matrix factorization (CNMF) and its sparse version, convolutive non-negative sparse coding (CNSC), are powerful, related tools. A particular difficulty of CNMF/CNSC, however, is the high demand on computing power and memory, which can prohibit their application to large scale tasks. In this talk, we propose an online algorithm for CNMF and CNSC, which processes input data piece-by-piece and updates the learned patterns after the processing of each piece by using accumulated sufficient statistics. The online CNSC algorithm remarkably increases converge speed of the CNMF/CNSC pattern learning, thereby enabling its application to large scale tasks.

Online Pattern Learning for Non-Negative Convolutive Sparse Coding

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