Blind Source Separation via Multinode Sparse Representation
نویسندگان
چکیده
We consider a problem of blind source separation from a set of instantaneous linear mixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in an appropriate representation according to some signal dictionary, dramatically improves the quality of separation. In this work we use the property of multi scale transforms, such as wavelet or wavelet packets, to decompose signals into sets of local features with various degrees of sparsity. We use this intrinsic property for selecting the best (most sparse) subsets of features for further separation. The performance of the algorithm is verified on noise-free and noisy data. Experiments with simulated signals, musical sounds and images demonstrate significant improvement of separation quality over previously reported results.
منابع مشابه
Image Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملBLIND SEPARATION OF COMPLEX−VALUED MIXTURES: SPARSE REPRESENTATION IN POLAR AND CARTESIAN SCATTER−PLOTS (MonPmOR1)
This study is concerned with reconstruction of complex−valued components comprising a linear mixing model of unknown real−valued sources, given a set of their complex−valued mixtures. We adopt previous results in the area of Blind Source Separation (BSS) of linear mixtures, based on sparse representation by means of a multiscale framework such as wavelet packets, and exploit the properties of s...
متن کاملSparse Representation and Its Applications in Blind Source Separation
In this paper, sparse representation (factorization) of a data matrix is first discussed. An overcomplete basis matrix is estimated by using the K−means method. We have proved that for the estimated overcomplete basis matrix, the sparse solution (coefficient matrix) with minimum l−norm is unique with probability of one, which can be obtained using a linear programming algorithm. The comparisons...
متن کاملSparse Component Analysis for Blind Source Separation with Less Sensors than Sources
A sparse decomposition approach of observed data matrix is presented in this paper and the approach is then used in blind source separation with less sensors than sources. First, sparse representation (factorization) of a data matrix is discussed. For a given basis matrix, there exist infinite coefficient matrices (solutions) generally such that the data matrix can be represented by the product...
متن کامل