TY - GEN
T1 - On the effectiveness of PARAFAC-based estimation for blind speech separation
AU - Mokios, Kleanthis N.
AU - Potarnianos, Alexandros
AU - Sidiropoulos, Nicholas D.
PY - 2008
Y1 - 2008
N2 - This work establishes the effectiveness of parallel factor (PARAFAC) analysis in blind speech separation (BSS) problems. The BSS problem is formulated as a conjugate-symmetric PARAFAC model that is fitted optimally, using an efficient alternating least-squares algorithm that converges monotonically. The identifiability properties of the model are also presented, revealing the much broader identifiability potential of joint-diagonalization- based BSS methods. In order to focus on estimation performance, perfect resolution of the permutation ambiguity is assumed. Simulations under varying reverberation conditions and comparison with previous estimation methods that are widely used in BSS problems demonstrate significant performance gains. Signal-to-interference (SIR) ratio improvement of over 27 dB is achieved using PARAFAC. Average SIR gains of 2.5 and 6.3 dB are achieved compared to state-of-the-art FastICA[2] and FDSOS (Parra's)[5] estimation algorithms, respectively.
AB - This work establishes the effectiveness of parallel factor (PARAFAC) analysis in blind speech separation (BSS) problems. The BSS problem is formulated as a conjugate-symmetric PARAFAC model that is fitted optimally, using an efficient alternating least-squares algorithm that converges monotonically. The identifiability properties of the model are also presented, revealing the much broader identifiability potential of joint-diagonalization- based BSS methods. In order to focus on estimation performance, perfect resolution of the permutation ambiguity is assumed. Simulations under varying reverberation conditions and comparison with previous estimation methods that are widely used in BSS problems demonstrate significant performance gains. Signal-to-interference (SIR) ratio improvement of over 27 dB is achieved using PARAFAC. Average SIR gains of 2.5 and 6.3 dB are achieved compared to state-of-the-art FastICA[2] and FDSOS (Parra's)[5] estimation algorithms, respectively.
KW - Blind speech separation
KW - Estimation method
KW - Non-stationary signals
KW - Parallel factor analysis
UR - http://www.scopus.com/inward/record.url?scp=51449106067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51449106067&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4517569
DO - 10.1109/ICASSP.2008.4517569
M3 - Conference contribution
AN - SCOPUS:51449106067
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 153
EP - 156
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -