Please use this identifier to cite or link to this item: https://doi.org/10.1109/89.784103
Title: Kalman-filtering speech enhancement method based on a voiced-unvoiced speech model
Authors: Goh, Z.
Tan, K.-C.
Tan, B.T.G. 
Issue Date: 1999
Citation: Goh, Z., Tan, K.-C., Tan, B.T.G. (1999). Kalman-filtering speech enhancement method based on a voiced-unvoiced speech model. IEEE Transactions on Speech and Audio Processing 7 (5) : 510-524. ScholarBank@NUS Repository. https://doi.org/10.1109/89.784103
Abstract: In this work, we are concerned with optimal estimation of clean speech from its noisy version based on a speech model we propose. We first propose a (single) speech model which satisfactorily describes voiced and unvoiced speech and silence (i.e., pauses between speech utterances), and also allows for exploitation of the long term characteristics of noise. We then reformulate the model equations so as to facilitate subsequent application of the well-established Kalman filter for computing the optimal estimate of the clean speech in the minimum-mean-square-error sense. Since the standard algorithm for Kalman filtering involves multiplications of very large matrices and thus demands high computational cost, we devise a mathematically equivalent algorithm which is computationally much more efficient, by exploiting the sparsity of the matrices concerned. Next, we present the methods we use for estimating the model parameters and give a complete description of the enhancement process. Performance assessment based on spectrogram plots, objective measures and informal subjective listening tests all indicate that our method gives consistently good results. As far as signal-to-noise ratio is concerned, the improvements over existing methods can be as high as 4 dB.
Source Title: IEEE Transactions on Speech and Audio Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/97015
ISSN: 10636676
DOI: 10.1109/89.784103
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