Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Evaluation of blind signal separation method using directivity pattern under reverberant conditions
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Blind source separation combining independent component analysis and beamforming
EURASIP Journal on Applied Signal Processing
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
A least-squares approach to blind channel identification
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Blind Separation of Underdetermined Convolutive Mixtures Using Their Time–Frequency Representation
IEEE Transactions on Audio, Speech, and Language Processing
Spatio–Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures
IEEE Transactions on Audio, Speech, and Language Processing
Blind Extraction of Dominant Target Sources Using ICA and Time-Frequency Masking
IEEE Transactions on Audio, Speech, and Language Processing
Cluster number selection for a small set of samples using the Bayesian Ying-Yang model
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A multistage approach to blind separation of convolutive speech mixtures
Speech Communication
Underdetermined DOA estimation via independent component analysis and time-frequency masking
Journal of Electrical and Computer Engineering
Blind source separation based on power spectral density
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
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In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well known k-means or fuzzy -cmeans clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by using k-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.