Speech Communication - Eurospeech '91
Study of a voice activity detector and its influence on a noise reduction system
Speech Communication
Interference cancellation using radial basis function networks
Signal Processing
Binaural sub-band adaptive speech enhancement using artifical neural networks
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Optimal and Adaptive Signal Processing
Optimal and Adaptive Signal Processing
Signal Processing - Special section: Hans Wilhelm Schüßler celebrates his 75th birthday
Adaptive Nonlinear Regression Using Multiple Distributed Microphones for In-Car Speech Recognition
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Speech Enhancement (Signals and Communication Technology)
Speech Enhancement (Signals and Communication Technology)
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Efficient alternatives to the Ephraim and Malah suppression rule for audio signal enhancement
EURASIP Journal on Applied Signal Processing
Multichannel direction-independent speech enhancement using spectral amplitude estimation
EURASIP Journal on Applied Signal Processing
Novel sub-band adaptive systems incorporating wiener filtering for binaural speech enhancement
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Monte Carlo smoothing with application to audio signal enhancement
IEEE Transactions on Signal Processing
A parallel neural network approach to prediction of Parkinson's Disease
Expert Systems with Applications: An International Journal
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This paper deals with the problem of enhancing the quality of speech signals, which has received growing attention in the last few decades. Many different approaches have been proposed in the literature under various configurations and operating hypotheses. The aim of this paper is to give an overview of the main classes of noise reduction algorithms proposed to-date, focusing on the case of additive independent noise. In this context, we first distinguish between single and multi channel solutions, with the former generally shown to be based on statistical estimation of the involved signals whereas the latter usually employ adaptive procedures (as in the classical adaptive noise cancellation scheme). Within these two general classes, we distinguish between certain subfamilies of algorithms. Subsequently, the impact of nonlinearity on the speech enhancement problem is highlighted: the lack of perfect linearity in related processes and the non-Gaussian nature of the involved signals are shown to have motivated several researchers to propose a range of efficient nonlinear techniques for speech enhancement. Finally, the paper summarizes (in tabular form) for comparative purposes, the general features, list of operating assumptions, the relative advantages and drawbacks, and the various types of nonlinear techniques for each class of speech enhancement strategy.