Tham khảo tài liệu 'artificial mind system – kernel memory approach - tetsuya hoya part 13', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Embodiment of Both the Sensation and LTM Modules 167 Fig. the performance of the combined complex ICA with the neural memory approach . ze 6 1 2 was compared to that of the conventional blind speech separation scheme Murata et al. 2001 . the plot shown by ye . As confirmed by the listening tests it is shown that the combined complex ICA with the neural memory approach yields a better performance in comparison with the conventional approach in Fig. it is remarkable . by examining the segments of y1 and Z1 between the sample numbers at around 15000 and 30000. A Further Consideration of the Blind Speech Extraction Model As described the neural memory within the blind speech extraction model as shown in Fig. can compensate for the problems of permutation and scaling ambiguity both of which are inherent to ICA. In the AMS context the subband ICA can be viewed as one of the pre-processing units within the sensory module to perform the speech extraction separation whilst the neural memory realised by the PNNs represents the LTM. Although a great number of approaches have been developed based upon the blind signal processing techniques such as ICA see . Cichocki and Amari 2002 to solve the cocktail party problems the study by Sagi et al. Sagi et al. 2001 treats this problem rather differently . within the context similar to pattern recognition identification. In the study they exploited sparse binary associative memories Hecht-Nielsen 1998 or what they call cortronic neural networks which simulate the functionality of the cerebral cortex and are trained by a Hebbian type learning algorithm albeit different from the one used in Chap. 4 and their model requires only a single microphone unlike most of the ICA approaches. Similar to the pattern recognition context as implied in Sagi et al. 2001 another model of blind speech extraction can be considered by exploiting the concept of learning in Chap. 7 and the LTM modules within the AMS .
