Graduate School and Research Center in Digital Sciences

Online non-negative convolutive pattern learning for speech signals

Wang, Dong; Vipperla, Ravichander; Evans, Nicholas; Zheng, Thomas Fang

IEEE Transactions on Signal Processing, Volume 61, N°1, 2013, ISSN: 1053-587X

The unsupervised learning of spectro-temporal patterns within speech signals is of interest in a broad range of applications. Where patterns are non-negative and convolutive in nature, relevant learning algorithms include convolutive non-negative matrix factorization (CNMF) and its sparse alternative, convolutive non-negative sparse coding (CNSC). Both algorithms, however, place unrealistic demands on computing power and memory which prohibit their application in large scale tasks. This paper proposes a new online implementation of CNMF and CNSC which processes input data piece-by-piece and updates learned patterns gradually with accumulated statistics. The proposed approach facilitates pattern learning with huge volumes of training data that are beyond the capability of existing alternatives. We show that, with unlimited data and computing resources, the new online learning algorithm almost surely converges to a local minimum of the objective cost function. In more realistic situations, where the amount of data is large and computing power is limited, online learning tends to obtain lower empirical cost than conventional batch learning.

Document Doi Bibtex

Title:Online non-negative convolutive pattern learning for speech signals
Keywords:Non-negative matrix factorization, convolutive NMF, online pattern learning, sparse coding, speech processing, speech recognition
Department:Digital Security
Eurecom ref:3820
Copyright: © 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Bibtex: @article{EURECOM+3820, doi = {}, year = {2012}, month = {12}, title = {{O}nline non-negative convolutive pattern learning for speech signals }, author = {{W}ang, {D}ong and {V}ipperla, {R}avichander and {E}vans, {N}icholas and {Z}heng, {T}homas {F}ang}, journal = {{IEEE} {T}ransactions on {S}ignal {P}rocessing, {V}olume 61, {N}°1, 2013, {ISSN}: 1053-587{X}}, url = {} }
See also: