TY - CHAP
T1 - Speech segregation using constrained ICA
AU - Lin, Qiu-Hua
AU - Zheng, Yong-Rui
AU - Yin, Fuliang
AU - Liang, Hua-Lou
PY - 2004/8/11
Y1 - 2004/8/11
N2 - In natural environment, speech often occurs concurrently with acoustic interference. How to effectively extract speech remains a great challenge. This paper describes a novel constrained Independent Component Analysis (ICA) approach, the ICA with reference (ICA-R), to speech segregation. Different from the traditional ICA which recovers simultaneously all the source signals, the ICA-R extracts only some desired source signals from the mixtures of source signals by incorporating some a priori information into the separation process. We show how the ICA-R can be applied to separate a target speech signal from interfering sounds by exploiting a proper reference signal, which is based on the different characteristic between speech signal and its environmental noises, i.e., the speech signal has pitch and its harmonic frequencies whereas the noises usually do not. Results of computer experiments demonstrate the efficiency of the proposed method.
AB - In natural environment, speech often occurs concurrently with acoustic interference. How to effectively extract speech remains a great challenge. This paper describes a novel constrained Independent Component Analysis (ICA) approach, the ICA with reference (ICA-R), to speech segregation. Different from the traditional ICA which recovers simultaneously all the source signals, the ICA-R extracts only some desired source signals from the mixtures of source signals by incorporating some a priori information into the separation process. We show how the ICA-R can be applied to separate a target speech signal from interfering sounds by exploiting a proper reference signal, which is based on the different characteristic between speech signal and its environmental noises, i.e., the speech signal has pitch and its harmonic frequencies whereas the noises usually do not. Results of computer experiments demonstrate the efficiency of the proposed method.
UR - https://www.scopus.com/pages/publications/33845430665
U2 - 10.1007/978-3-540-28647-9_124
DO - 10.1007/978-3-540-28647-9_124
M3 - Chapter in an edited book (as author)
AN - SCOPUS:33845430665
SN - 9783540228417
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 755
EP - 760
BT - Advances in Neural Networks - ISNN 2004
A2 - Yin, Fuliang
A2 - Wang, Jun
A2 - Guo, Chengan
PB - Springer Verlag
ER -