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In this paper, a neuro-fuzzy system is proposed to remove multifrequency noise from audio signals. There are two major elements in our method. The first comprises a fuzzy cerebellar model articulation controller (FCMAC) that is used to quantize the signals. The second one is developed based on the theory of stochastic real values (SRV) that is used to search the optimal frequencies for the overall trained system. We present a DSP implementation of the SRV algorithm and results on its performance in removing spectral noise that is buried in audio signals.
Author (s): Lin, Ching-Shun; Kyriakakis, Chris
Affiliation:
Immersive Audio Laboratory, Integrated Media Systems Center, Univ. of Southern California, Los Angeles, CA
(See document for exact affiliation information.)
AES Convention: 115
Paper Number:5966
Publication Date:
2003-10-06
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Session subject:
Archiving and Restoration
Permalink: https://aes2.org/publications/elibrary-page/?id=12391
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Lin, Ching-Shun; Kyriakakis, Chris; 2003; Multi-Frequency Noise Removal Based on Reinforcement Learning [PDF]; Immersive Audio Laboratory, Integrated Media Systems Center, Univ. of Southern California, Los Angeles, CA; Paper 5966; Available from: https://aes2.org/publications/elibrary-page/?id=12391
Lin, Ching-Shun; Kyriakakis, Chris; Multi-Frequency Noise Removal Based on Reinforcement Learning [PDF]; Immersive Audio Laboratory, Integrated Media Systems Center, Univ. of Southern California, Los Angeles, CA; Paper 5966; 2003 Available: https://aes2.org/publications/elibrary-page/?id=12391