Sparse Source Separation with Unknown Source Number
نویسندگان
چکیده
Sparse Blind Source Separation (BSS) problems have recently received some attention. And some of them have been proposed for the unknown number of sources. However, they only consider the overdetermined case (i.e. with more sources than sensors). In the practical BSS, there are not prior assumptions on the number of sources. In this paper, we use cluster and Principal Component Analysis (PCA) to estimate the number of the sources and the separation matrix, and then make the estimation of sources. Experiments with speech signals demonstrate the validity of the proposed method.
منابع مشابه
Blind Separation of Infinitely Many Sparse Sources
This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method witho...
متن کاملBayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources
SUMMARY This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, estimates the direction of arrival of each source, and performs permutation alignment. We confirmed experimentally that reasonably...
متن کاملUnderdetermined blind separation of sparse sources with instantaneous and convolutive mixtures
We consider the underdetermined blind source separation problem with linear instantaneous and convolutive mixtures when the input signals are sparse, or have been rendered sparse. In the underdetermined case the problem requires solving three subproblems: detecting the number of sources, estimating the mixing matrix, and finding an adequate inversion strategy to obtain the sources. This paper s...
متن کاملOver-complete blind source separation by applying sparse decomposition and information theoretic based probabilistic approach
Both in the case of cellular communication and in the case of spoken dialogue based information retrieval systems on mobile platforms there exist a number of interference signals. Therefore, it is essential to separate these interference signals from the intended signal(s) in order to have clear communication in the case of cellular phone and to improve the speech recognition accuracy in the ca...
متن کاملSparse Source Separation Using Discrete Prior Models
In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with a deterministic but unknown spectral amplitude. The Bernoulli variables are modeled in turn by first order Markov processes with transition probabilities learned from a training database. We consi...
متن کامل