INTERSPEECH 2011
12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Online Pattern Learning for Non-Negative Convolutive Sparse Coding

Dong Wang, Ravichander Vipperla, Nicholas Evans

EURECOM, France

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 paper, 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.

Full Paper

Bibliographic reference.  Wang, Dong / Vipperla, Ravichander / Evans, Nicholas (2011): "Online pattern learning for non-negative convolutive sparse coding", In INTERSPEECH-2011, 65-68.