Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2011 | 12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5 | non-negative convolutive sparse coding, online pattern learning |
Field | DocType | Citations |
Online algorithm,Pattern learning,Pattern recognition,Neural coding,Computer science,Matrix decomposition,Speech recognition,Unsupervised learning,Artificial intelligence,Sufficient statistic | Conference | 7 |
PageRank | References | Authors |
0.58 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Wang | 1 | 375 | 39.86 |
| Ravichander Vipperla | 2 | 63 | 6.16 |
| nicholas evans | 3 | 594 | 54.41 |