The extraction of a desired speech signal from a noisy environment has become a challenging issue. In the recent years, the scientific community has particularly focused on multichannel techniques which are dealt with in this review. In fact, this study tries to classify these multichannel techniques into three main ones: Beamforming, Independent Component Analysis (ICA) and Time Frequency (T-F) masking. This paper also highlights their advantages and drawbacks. However these previously mentioned techniques could not afford satisfactory results. This fact leads to the idea that a combination of those techniques, which is depicted along this study, may probably provide more efficient results. Indeed, giving the fact that those approaches are still be considered as being not totally efficient, has led us to review these mentioned above in the hope that further researches will provide this domain with suitable innovations.
M. Brandstein and D. Ward, “Microphone Arrays: Signal Processing Techniques and Applications,” Digital Signal Processing, 2001, Springer.
C. Cherry: “Some Experiments on the Recognition of Speech, with One and with Two Ears,” Journal of the Acoustical Society of America, Vol. 25, No. 5, 1953, pp. 975–979. doi:10.1121/1.1907229
S. Haykin and Z. Chen, “The Cocktail Party Problem,” Journals of Neural Computation, Vol. 17, No. 9, 2005, pp. 1875-1902. doi:10.1162/0899766054322964
D. L. Wang and G. J. Brown, “Computational Auditory Scene Analysis: Principles Algorithms and Applications,” Wiley, New York, 2006. 10.1109/TNN.2007.913988
J. Benesty, S. Makino and J. Chen, “Speech Enhancement,” Signal and Communication Technology, Springer, Berlin, 2005.
S. Douglas and M. Gupta, “Convolutive Blind Source Separation for Audio Signals,” Blind Speech Separation, Springer, Berlin, 2007. doi:10.1007/978-1-4020-6479-1_1
H. Sawada, S. Araki and S. Makino “Frequency-Domain Blind Source Separation,” Blind Speech Separation, Springer, Berlin, 2007. doi:10.1007/3-540-27489-8_13
S. Markovich, S. Gannot and I. Cohen, “Multichannel Eigen Space Beamforming in a Reverberant Noisy Environment with Multiple Interfering Speech Signals,” IEEE Transactions on Audio, Speech, and Language Processing, Vol. 17, No. 6, 2009, pp. 1071-1086. doi:10.1109/TASL.2009.2016395
M. A. Dmour and M. Davies “A New Framework for Underdetermined Speech Extraction Using Mixture of Beamformers,” IEEE Transactions on Audio, Speech, and Language Processing, Vol. 19, No. 3, 2011, pp. 445-457. doi:10.1109/TASL.2010.2049514
J. Benesty, J. Chen and Y. Huang, “Conventional Beamforming Techniques,” Microphone Array Signal Processing, Springer, Berlin, 2008. doi:10.1121/1.3124775
V. G. Reju, S. N. Koh and I. Y. Soon, “Underdetermined Convolutive Blind Source Separation via Time-Frequency Masking,” IEEE Transactions on Audio, Speech, and Language Processing, Vol. 18, No. 1, 2010, pp. 101-116. doi:10.1109/TASL.2009.2024380
O. Yilmaz and S. Rickard, “Blind Separation of Speech Mixtures via Time-Frequency Masking,” IEEE Transactions on Signal Processing, Vol. 52, 2004, pp. 1830-1847. doi:10.1109/TSP.2004.828896
J. Freudenberger and S. Stenzel, “Time-Frequency Masking for Convolutive and Noisy Mixtures,” Workshop on Hands-Free Speech Communication and Microphone Arrays, 2011, pp. 104-108. doi:10.1109/HSCMA.2011.5942374
T. Jan, W. Wang and D. L. Wang, “A Multistage Approach to Blind Separation of Convolutive Speech Mixtures,” Speech Communication, Vol. 53, 2011, pp. 524-539. doi:10.1016/j.specom.2011.01.002
J. Cermak, S. Araki, H. Sawada and S. Makino, “Blind Speech Separation by Combining Beamformers and a Time Frequency Binary Mask,” IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, 2007, pp. I-145-I-148.
O. Frost, “An Algorithm for Linearly Constrained Adaptive Array Processing,” Proceedings of the IEEE, Vol. 60, No. 8, 1972, pp. 926-935.
E. A. P. Habets, J. Benesty, I. Cohen, S. Gannot and J. Dmochowski, “New Insights into the MVDR Beamformer in Room Acoustics,” IEEE Transactions on Audio, Speech, and Language Processing, 2010, pp. 158-170. doi:10.1109/TASL.2009.2024731
L. Griffiths and C. Jim, “An Alternative Approach to Linearly Constrained Adaptive Beamforming,” IEEE Transactions on Antennas and Propagation, Vol. 30, No. 1, 1982, pp. 27-34. doi:10.1109/TAP.1982.1142739
S. Gannot and I. Cohen “Adaptive Beamforming and Post filtering,” Speech Processing, Springer, Berlin, 2007, pp. 199-228.
A. Spriet, M. Moonen and J. Wouters, “Spatially Pre-Processed Speech Distortion Weighted Multi-Channel Wiener Filtering for Noise Reduction,” Signal Processing, Vol. 84, No. 12, 2004, pp. 2367-2387. doi:10.1016/j.sigpro.2004.07.028
P. Comon, “Independent Component Analysis, a New Concept,” Signal Processing, Vol. 36, No, 3, 1994, pp. 287-314. doi:10.1016/0165-1684(94)90029-9
Z. Koldovsky and P. Tichavsky, “Time-Domain Blind Audio Source Separation Using Advanced ICA Methods,” Interspeech, Antwerp Belgium, 2007, pp. 846-849.
S. Makino, H. Sawada, R. Mukai and S. Araki, “Blind Source Separation of Convolutive Mixtures of Speech in Frequency Domain,” IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, No. 7, 2005, pp. 1640-1655. doi:10.1093/ietfec/e88-a.7.1640
A. Sarmiento, I. Durán-Díaz, S. Cruces and P. Aguilera, “Generalized Method for Solving the Permutation Problem in Frequency-Domain Blind Source Separation of Convolved Speech Signals,” Interspeech, 2011, pp. 565-568.
R. Mazur and A. Mertins, “A Sparsity Based Criterion for Solving the Permutation Ambiguity in Convolutive Blind Source Separation,” IEEE International Conference on Acoustics, Speech and Signal Processing, Prague Czech Republic, 2011, pp. 1996-1999. doi:10.1109/ICASSP.2011.5946902
H. Sawada, R. Mukai, S. Araki and S. Makino, “A Robust and Precise Method for Solving the Permutation Problem of Frequency-Domain Blind Source Separation,” IEEE Transactions on Speech and Audio Processing, Vol. 12, No. 5, 2004, pp. 530-538. doi:10.1109/TSA.2004.832994
M. S. Pedersen, J. Larsen, U. Kjems and L. C. Parra, “A Survey of Convolutive Blind Source Separation Methods,” Handbook on Speech Processing and Speech Communication, Springer, Berlin, 2007.
S. Rickard, “The DUET Blind Source Separation Algorithm,” Blind Speech Separation, Springer, Berlin, 2007. doi:10.1007/978-1-4020-6479-1_8
A. Jourjine, S. Rickard and O. Yilmaz, “Blind Separation of Disjoint Orthogonal Signals: Demixing n Sources from 2 Mixtures,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 5, 2000, pp. 2985-2988. doi:10.1109/ICASSP.2000.861162
S. Araki, H. Sawada and S. Makino, “K-Means Based Underdetermined Blind Speech Separation,” Blind Speech Separation, Springer, Berlin, 2007. doi:10.1007/978-1-4020-6479-1_9
R. O. Duda, P. E. Hart and D. G. Stork, “Pattern Classification,” Wiley & Sons Ltd., New York, 2000.
M. S. Pedersen, D. L. Wang, J. Larsen and U. Kjems “Two-Microphone Separation of Speech Mixtures,” IEEE Transactions on Neural Networks, Vol. 19, No. 3, 2008, pp. 475-492. doi:10.1109/TNN.2007.911740
D. L. Wang, “On Ideal Binary Mask as the Computational Goal of Auditory Scene Analysis,” Speech Separation by Humans and Machines, Springer, Berlin, 2005, pp. 181-197. doi:10.1007/0-387-22794-6_12
I. Jafari, R. Togneri and S. Nordholm, “Review of Multi-Channel Source Separation in Realistic Environments,” 13th Australasian International Conference on Speech Science and Technology, Melbourne, 14-16 December 2010, pp. 201-204.
S. Araki and T. Nakatani, “Hybrid Approach for Multichannel Source Separation Combining Time Frequency Mask with Multi-Channel Wiener Filter,” IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, 22-27 May 2011, pp. 225-228. doi:10.1109/ICASSP.2011.5946381
L.Wang, H. Ding and F. Yin, “Target Speech Extraction in Cocktail Party by Combining Beamforming and Blind Source Separation,” Journal Acoustics Australia, Vol. 39, No. 2, 2011, pp. 64-68.