In this paper we focus on the two-stage underdetermined blind source separation (BSS), which consists of the mixing matrix estimation stage, the first stage, and the source estimation stage, the second stage. In the first stage, both the mixing matrix and the number of sources are estimated by a new potential-function-based clustering method using a new potential function constructed by Laplacian-like window function. In the second stage, in order to overcome the disadvantage of 11-norm solution, a new sparse representation based on high-order statistics in transformed domain, which is called statistically sparse component analysis (SSCA), is proposed to recover the sources. Compared with the existing two-stage methods, the proposed approach can achieve higher reconstructed signalto-noise ratios (SNRs).
The number of medical images produced in cardiology and radiology department etc. is rising strongly. New challenges arise in efficient medical data management issues, such as information retrieval and security. A novel method of combining watermarking annotation with independent content feature (ICF) for medical image retrieval is proposed. ICF is extracted by independent component analysis (ICA) to represent medical images, and the digital watermark carrying patient' information text is imperceptibly embedded. Experimental results show that the scheme employs locally salient information from medical image, and it has good retrieval performance, and watermarking algorithm used in the scheme has robustness property to the JPEG compression.
Blind source separation for convolutive mixtures can be solved effectively in the frequency domainwhere independent component analysis is performed in each frequency independently. However, the permutation problem arises: the permutation ambiguity of ICA in each frequency bin should be aligned so that a separated signal in the time-domain contains frequency components of the same source signal. In this paper, we present a new method for solving the permutation problem using microphone sub-arrays. It is based on the combination of two approaches: direction of arrival (DOA) estimation for sources and the inter-frequency correlation of signal envelopes. First, DOA estimation is performed using microphone sub-arrays so that the permutation problem is solved more robustly in low frequencies. Second, we exploit the correlation between the adjacent bins to fix the permutation for the remaining frequencies. Experimental results show that the proposed method provided a more robust solution to the permutation problem in a real acoustic environment.
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