Multi-Frequency Noise Removal Based on Reinforcement Learning

Lin CS, Kyriakakis C. Multi-Frequency Noise Removal Based on Reinforcement Learning. In: AES Convention 115. Audio Engineering Society; 2003. Paper 5966. Available from: https://aes.org/publications/elibrary-page/?id=12391

@inproceedings{Lin2003_12391,
  author = {Lin, Ching-Shun and Kyriakakis, Chris},
  title = {{Multi-Frequency Noise Removal Based on Reinforcement Learning}},
  booktitle = {AES Convention 115},
  note = {Paper 5966},
  year = {2003},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=12391}
}

TY  - CPAPER
TI  - Multi-Frequency Noise Removal Based on Reinforcement Learning
AU  - Lin, Ching-Shun
AU  - Kyriakakis, Chris
T2  - AES Convention 115
M1  - Paper 5966
PY  - 2003
DA  - 2003/10/06
UR  - https://aes.org/publications/elibrary-page/?id=12391
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -