Multirate systems and filter banks
Multirate systems and filter banks
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Multirate Digital Signal Processing: Multirate Systems, Filter Banks, Wavelets
Multirate Digital Signal Processing: Multirate Systems, Filter Banks, Wavelets
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Digital Signal Processing (4th Edition)
Digital Signal Processing (4th Edition)
Speech Enhancement
Spectrum estimation by wavelet thresholding of multitaperestimators
IEEE Transactions on Signal Processing
IEEE Communications Magazine
Estimation of power spectral density using wavelet thresholding
CSECS'08 Proceedings of the 7th conference on Circuits, systems, electronics, control and signal processing
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The paper describes the design of a new single-channel method for speech enhancement that employs the wavelet transform. Signal decomposition is currently performed in the time domain while noise is removed on individual decomposition levels using thresholding techniques. Here the wavelet transform is applied in the spectral domain. Used as the basis is the method of spectral subtraction, which is suitable for real-time implementation because of its simplicity. The greatest problem in the spectral subtraction method is a trustworthy noise estimate, in particular when non-stationary noise is concerned. Using the wavelet transform we can achieve a more accurate power spectral density also of noise that is non-stationary. Listening tests and SNR measurements yield satisfactory results in comparison with earlier reported experience.