Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Joint anti-diagonalization for blind source separation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Separating more sources than sensors using time-frequency distributions
EURASIP Journal on Applied Signal Processing
Blind separation of nonstationary sources based on spatial time-frequency distributions
EURASIP Journal on Applied Signal Processing
Underdetermined blind source separation based on relaxed sparsity condition of sources
IEEE Transactions on Signal Processing
Blind separation of mutually correlated sources using precoders
IEEE Transactions on Neural Networks
Fourth-order blind identification of underdetermined mixtures of sources (FOBIUM)
IEEE Transactions on Signal Processing
Analysis and synthesis of multicomponent signals using positivetime-frequency distributions
IEEE Transactions on Signal Processing
Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
Sequential blind extraction of instantaneously mixed sources
IEEE Transactions on Signal Processing
Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Blind Separation of Underdetermined Convolutive Mixtures Using Their Time–Frequency Representation
IEEE Transactions on Audio, Speech, and Language Processing
Blind extraction of singularly mixed source signals
IEEE Transactions on Neural Networks
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
Maximum contrast analysis for nonnegative blind source separation
Computers & Mathematics with Applications
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In this paper, we consider the problem of underdetermined blind source separation (UBSS), i.e., there are more sources than mixtures. By exploiting the time-frequency (TF) sparsity of the source signals, some TF-UBSS algorithms have recently been proposed in the literature. These algorithms require that the number of active sources at any TF point should not exceed one or be strictly less than the number of mixtures. In this paper, we show that if the number of sources is greater than the number of mixtures by one, the sparsity assumption can be further relaxed. Especially, it is allowed to have as many sources as mixtures at any TF point in this case. Then we propose a new TF-UBSS method to recover the sources. The relaxation on the maximum number of active sources at TF points ensures that the TF-based methods can be used in a wider range of applications.