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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yu Shao Chip-Hong Chang |
| Copyright Year | 2005 |
| Description | Author affiliation: Center for Integrated Circuits & Syst., Nanyang Technol. Univ., Singapore, Singapore (Yu Shao; Chip-Hong Chang) |
| Abstract | This paper presents a new speech enhancement system. A psychoacoustic model is incorporated into the wavelet denoising technique to combat different adverse noise conditions. The system is composed of a feed-forward subsystem connected to the perceptual wavelet transform (PWT) processor, a soft-threshold based denoising scheme, and an unvoiced speech enhancement. The noisy speech is first decomposed into critical bands by the PWT from which a set of weights is extracted by the feed-forward subsystem. The average normalized time-frequency energy is used to guide the feedforward subsystem thresholding to reduce the stationary noise while the non-stationary and correlated noises are reduced by an improved wavelet denoising technique with soft-thresholding. Finally, the unvoiced speech enhancement is applied to further improve the intelligibility of the processed speech signal. Simulation results showed that this new system is capable of reducing noise with little speech degradation and the overall performance is superior to several competitive methods in both objective and subjective evaluations. |
| Starting Page | 864 |
| Ending Page | 867 |
| File Size | 479945 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780388348 |
| DOI | 10.1109/ISCAS.2005.1464725 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-05-23 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Speech enhancement Noise reduction Psychoacoustic models Feedforward systems Wavelet transforms Time frequency analysis Speech processing Signal processing Speech analysis Degradation |
| Content Type | Text |
| Resource Type | Article |
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