Digital Audio Restoration: A Statistical Model Based Approach
Digital Audio Restoration: A Statistical Model Based Approach
Towards a perceptually optimal spectral amplitude estimator for audio signal enhancement
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model
EURASIP Journal on Applied Signal Processing
Speech spectral amplitude estimators using optimally shaped Gamma and Chi priors
Speech Communication
Speech Enhancement Based on MAP Estimation Using a Variable Speech Distribution
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Proceedings of the 3rd International Universal Communication Symposium
A speech enhancement algorithm based on a chi MRF model of the speech STFT amplitudes
IEEE Transactions on Audio, Speech, and Language Processing
Nonlinear speech enhancement: an overview
Progress in nonlinear speech processing
Computer Speech and Language
Proceedings of the second workshop on eHeritage and digital art preservation
An evaluation study on speech feature densities for Bayesian estimation in robust ASR
Proceedings of the Third COST 2102 international training school conference on Toward autonomous, adaptive, and context-aware multimodal interfaces: theoretical and practical issues
Speech enhancement based on Sparse Code Shrinkage employing multiple speech models
Speech Communication
Speech enhancement using hidden Markov models in Mel-frequency domain
Speech Communication
An efficient solution to improve the spectral noise suppression rules
Digital Signal Processing
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Audio signal enhancement often involves the application of a time-varying filter, or suppression rule, to the frequency-domain transform of a corrupted signal. Here we address suppression rules derived under a Gaussian model and interpret them as spectral estimators in a Bayesian statistical framework. With regard to the optimal spectral amplitude estimator of Ephraim and Malah, we show that under the same modelling assumptions, alternative methods of Bayesian estimation lead to much simpler suppression rules exhibiting similarly effective behaviour. We derive three of such rules and demonstrate that, in addition to permitting a more straightforward implementation, they yield a more intuitive interpretation of the Ephraim and Malah solution.