Tianliang Peng

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We propose a new method for underdetermined blind source separation based on the time–frequency domain. First, we extract the time–frequency points that are occupied by a single source, and then, we use clustering methods to estimate the mixture matrix A. Second, we use the parallel factor (PARAFAC), which is based on nonnegative tensor factorization, to(More)
Under-determined mixtures in blind source separation (BSS) are characterized by the case that they have more inputs than outputs. The classical independent component analysis (ICA) methods cannot be applied to the under-determined case. However, sparseness-based approaches can be applied to the under-determined BSS. Two steps method has been widely employed(More)
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