CMAC spectral subtraction for speech enhancement
One of the major problems in speech signal enhancement and cancellation of additive noise is the availability of a reference signal. A comprehensive and efficient technique for speech enhancement based an extension of the spectral subtraction method is developed. In our proposed model, enhancement...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2001
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38205/ http://irep.iium.edu.my/38205/ http://irep.iium.edu.my/38205/1/CMAC_spectral_subtraction_for_speech_enhancement.pdf |
| Summary: | One of the major problems in speech signal enhancement and
cancellation of additive noise is the availability of a reference signal. A comprehensive and efficient technique for speech enhancement based an extension of the spectral subtraction method is developed. In our proposed model, enhancement is achieved by using a class of associative memory based on the cerebellar model arithmetic computer (CMAC) as a robust method to estimate the reference signal. CMAC can learn very fast and it can approximate a wide variety of non-linear functions. Thus the learning algorithm of CMAC can be integrated with the spectral subtraction method to produce a system that allows the noise estimate to be learned adaptively. The effectiveness of the architecture is demonstrated on a speech corrupted with very low signal to noise ratio (from -5db to -20db) on a vehicular environment. |
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