Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Computers in Biology and Medicine
Autoregressive decomposition and pole tracking applied to vocal fold nodule signals
Pattern Recognition Letters
A parallel neural network approach to prediction of Parkinson's Disease
Expert Systems with Applications: An International Journal
Computers and Electrical Engineering
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The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito's distance. Results have shown that Kalman's filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito's distance by up to four times.