Speech detection in noisy environments by wavelet energy-based recurrent neural fuzzy network |
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| Authors: | Chia-Feng Juang Chun-Nan Cheng Tai-Mao Chen |
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| Affiliation: | 1. Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan;2. Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan;3. Dept. of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan;4. Research Center for Information Technology Innovation, Academia Sinica, Taipei 115, Taiwan;5. Graduate Institute of Communication Engineering, National Chung Hsing University, Taichung 402, Taiwan;1. Institute of Chemical Process Fundamentals v.v.i., Czech Academy of Sciences, Prague, Czech Republic;2. Department of Chemical Engineering, National Chung Hsing University, Taichung 402, Taiwan;1. School of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi’an, Shaanxi 710038, China;2. Center for Information Engineering Science Research, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China;3. School of People Armed Police Engineering University, Xi’an, Shaanxi 710086, China;1. Harvard Medical School – Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA;2. Institute of Computer Science, P.J. ?afárik University, Ko?ice, Slovakia;3. Hearing Research Center, Boston University, Boston, MA, USA;4. Brain and Mind Laboratory, Department of Biomedical Engineering and Computational Science (BECS), Aalto University, Espoo, Finland |
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| Abstract: | This paper proposes a new speech detection method by recurrent neural fuzzy network in variable noise-level environments. The detection method uses wavelet energy (WE) and zero crossing rate (ZCR) as detection parameters. The WE is a new and robust parameter, and is derived using wavelet transformation. It can reduce the influences of different types of noise at different levels. With the inclusion of ZCR, we can robustly and effectively detect speech from noise with only two parameters. For detector design, a singleton-type recurrent fuzzy neural network (SRNFN) is proposed. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and the recurrent property makes them suitable for processing speech patterns with temporal characteristics. The learning ability of SRNFN helps avoid the need of empirically determining a threshold in normal detection algorithms. Experiments with different types of noises and various signal-to noise ratios (SNRs) are performed. The results show that using the WE and ZCR parameters-based SRNFN, a pretty good performance is achieved. Comparisons with another robust detection method, the refined time–frequency-based method, and other detectors have also verified the performance of the proposed method. |
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