NDLI: The Separated Speech Signals Combined the Hybrid Adaptive Algorithms by Using Power Spectral Density and Total Harmonic Distortion

Content Provider	IEEE Xplore Digital Library
Author	Yu-Jen Chen Gwo-Jiun Horng Gwo-Jia Jong
Copyright Year	2007
Description	Author affiliation: Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung (Yu-Jen Chen; Gwo-Jiun Horng; Gwo-Jia Jong)
Abstract	This paper presents the results of simulation experiments that successfully demonstrate frequency modulation (FM) co-channel speech signals separation by adopting the concepts of power spectral density (PSD) and total harmonic distortion (THD) analyzing adaptive filter combined Fuzzy logic algorithm with the amplitude-locked loop (ALL) separation system in order to improve the signal distortion, co-channel interferences (CCI) of the additive white Gaussian noise (AWGN). The advantage of ALL does not require coding process from modulation, when signals are conveyed by co-channel transmission. In traditional Kalman filter, we find some drawbacks such as low convergence speed and large mean square error (MSE). Hence, we adopted the proposed adaptive algorithm for improving the convergence speed and the MSE value. The improvement simulation results are included separated output signals, the analyses of PSD, THD of the signals, the relation between co-channel signal-to-noise ratio (SNRC), and MSE. It is found the performance of Kalman filter combined Fuzzy algorithm better than other algorithms.
Starting Page	825
Ending Page	830
File Size	314164
Page Count	6
File Format	PDF
ISBN	0769527779
DOI	10.1109/MUE.2007.207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-26
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Total harmonic distortion Analytical models Frequency modulation Speech analysis Source separation AWGN Adaptive algorithm Speech enhancement Signal analysis Convergence
Content Type	Text
Resource Type	Article

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