In most speech communication systems, the presence of background noise causes the quality and intelligibility of speech to degrade, especially when the Signal-to-Noise Ratio (SNR) is low. Numerous speech enhancement techniques have been employed successfully in many applications. However, at low signal-to-noise ratios most of these speech enhancement techniques tend to introduce a perceptually annoying residual noise known as "musical noise". The research presented in this thesis aims to minimize this musical noise and maximize the noise reduction ability of speech enhancement algorithms to improve speech quality in low SNR environments. This thesis proposes two novel speech enhancement algorithms based on Weiner and Kalman filters, and exploit the masking properties of the human auditory system to reduce background noise. The perceptual Wiener filter method uses either temporal or simultaneous masking to adjust the Wiener gain in order to suppress noise below the masking thresholds. The second algorithm involves reshaping the corrupted signal according to the masking threshold in each critical band, followed by Kalman filtering. A comparison of the results from these proposed techniques with those obtained from traditional methods suggests that the proposed algorithms address the problem of noise reduction effectively while decreasing the level of the musical noise. In this thesis, many other existing competitive noise suppression methods have also been discussed and their performance evaluated under different types of noise environments. The performances were evaluated and compared to each other using both objective PESQ measures (ITU-T P.862) and subjective listening tests (ITU-T P.835). The proposed speech enhancement schemes based on the auditory masking model outperformed the other methods that were tested.
| Identifer | oai:union.ndltd.org:ADTP/258283 |
| Date | January 2007 |
| Creators | Wang , Yao, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW |
| Publisher | Awarded by:University of New South Wales. Electrical Engineering & Telecommunications |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Copyright Wang Yao., http://unsworks.unsw.edu.au/copyright |
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