{'fraction_non_alphanumeric': 0.015017667844522967, 'fraction_numerical': 0.0, 'mean_word_length': 5.744047619047619, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Adaptive Semiblind Signal Separation Approach Using Temporal Structure of Sources

An adaptive semiblind signal separation algorithm for the separation of a class of arbitrarily distributed but temporally correlated source signals is introduced in this paper. The proposed adaptive algorithm is based only on second-order statistical(SOS) information and exploits the assumption that the source signals are not strictly spatial statistically independent, and temporal do not identical distributed too. Furthermore, the novel adaptive batch blind signal separation(BSS) method give an adaptive algorithm for correlated source signals. In order to get the ideal separation, the source signals must have different power spectra shapes. Simulation results for real image recordings show that the proposed algorithm has the ability to restore source signals, and its performance is comparable to that of the BSS algorithm such as AMUSE and JADE algorithm. The proposed algorithm is also shown to alleviate the limitation that the classic independent component analysis(ICA) algorithm can separate at most one Gaussian distributed source.