• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.413-414
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• 2007

Successful speech recognition in noisy environments for intelligent robots depends on the performance of preprocessing elements employed. We propose an architecture that effectively combines adaptive beamforming (ABF) and blind source separation (BSS) algorithms in the spatial domain to avoid permutation ambiguity and heavy computational complexity. We evaluated the structure and assessed its performance with a DSP module. The experimental results of speech recognition test shows that the proposed combined system guarantees high speech recognition rate in the noisy environment and better performance than the ABF and BSS system.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.199-206
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• 2004

This paper proposes a hybrid independent component analysis(ICA) of fixed-point(FP) algorithm and robust algorithm. The FP algorithm is applied for improving the analysis speed and performance, and the robust algorithm is applied for preventing performance degradations by means of very small kurtosis and temporal correlations between components. And the adaptive adaptation of temporal correlations has been proposed for solving limits of the conventional robust algorithm dependent on the maximum time delay. The proposed ICA has been applied to the problems for separating the 4-mixed signals of 500 samples and 10-mixed images of $512\times512$pixels, respectively. The experimental results show that the proposed ICA has a characteristics of adaptively adapting the maximum time delay, and has a superior separation performances(speed, rate) to conventional FP-ICA and hybrid ICA of heuristic correlation. Especially, the proposed ICA gives the larger degree of improvement as the problem size increases.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.417-422
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• 2000

In this paper, an efficient fuzzy rule generation scheme for adaptive neuro-fuzzy system modeling using the Independent Component Analysis(ICA) as a preprocessing is proposed. Correlation between inputs was not considered in the conventional neuro- fuzzy modeling schemes, such that enormous number of rules and large amount of error were unavoidable. The correlation between inputs is weakened by employing ICA so that the number of rules and the amount of error are reduced. In simulation, the Box-Jenkins furnace data is used to verify the effectiveness of the proposed method.