Sequential MCMC Methods for Audio Signal Enhancement

With the aim of addressing audio signal restoration as a sequential inference problem, we build upon Gabor regression to propose a state-space model for audio time series. Exploiting the structure of our model, we devise a sequential Markov chain Monte Carlo algorithm to explore the sequence of filt...

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Bibliographic Details
Published inICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 856 - 860
Main Authors Claveria, Ruben M., Godsill, Simon J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.05.2022
Subjects
Audio denoising
Bayesian filtering
Gabor frames
Inference algorithms
Monte Carlo methods
Noise reduction
Sequential MCMC
Signal processing algorithms
Signal restoration
Speech enhancement
Time series analysis
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Summary:With the aim of addressing audio signal restoration as a sequential inference problem, we build upon Gabor regression to propose a state-space model for audio time series. Exploiting the structure of our model, we devise a sequential Markov chain Monte Carlo algorithm to explore the sequence of filtering distributions of the synthesis coefficients. The algorithm is then tested on a series of denoising examples. Results suggest that the sequential approach is competitive with batch strategies in terms of perceptual quality and signal-to-noise ratio, while showing potential for real-time applications.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9747811