TY - GEN
T1 - Underdetermined blind source separation based on continuous density hidden Markov models
AU - Zhu, Xiaoming
AU - Parhi, Keshab K.
PY - 2010
Y1 - 2010
N2 - In this paper, a novel method is developed to solve the problem of underdetermined blind source separation, where the number of mixtures is smaller than that of sources. Generalized Gaussian Distributions (GGDs) are used to model the source signals and generative Continuous Density Hidden Markov Models (CDHMMs) are derived to track the nonstationarity inside the source signals. Each source signal can switch between several states such that the separation performance can be significantly improved. The model parameters are trained through the Expectation Maximization (EM) algorithm and the source signals are estimated via the Maximum a Posteriori (MAP) approach. Compared with the results of L1-norm solution, our proposed algorithm has obtained much better output signal-to-noise ratio (SNR) and the separation results are more realistic.
AB - In this paper, a novel method is developed to solve the problem of underdetermined blind source separation, where the number of mixtures is smaller than that of sources. Generalized Gaussian Distributions (GGDs) are used to model the source signals and generative Continuous Density Hidden Markov Models (CDHMMs) are derived to track the nonstationarity inside the source signals. Each source signal can switch between several states such that the separation performance can be significantly improved. The model parameters are trained through the Expectation Maximization (EM) algorithm and the source signals are estimated via the Maximum a Posteriori (MAP) approach. Compared with the results of L1-norm solution, our proposed algorithm has obtained much better output signal-to-noise ratio (SNR) and the separation results are more realistic.
KW - Generalized Gaussian distribution
KW - Hidden Markov model
KW - Nonstationary signals
KW - Underdetermined blind source separation
UR - http://www.scopus.com/inward/record.url?scp=78049392547&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049392547&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2010.5495730
DO - 10.1109/ICASSP.2010.5495730
M3 - Conference contribution
AN - SCOPUS:78049392547
SN - 9781424442966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4126
EP - 4129
BT - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Y2 - 14 March 2010 through 19 March 2010
ER -