Title :
Joint Bayesian removal of impulse and background noise
Author :
Murphy, James ; Godsill, Simon
Author_Institution :
Dept. of Eng., Cambridge Univ., Cambridge, UK
Abstract :
We present a method for the removal of noise including non-Gaussian impulses from a signal. Impulse noise is removed jointly a homogenous Gaussian noise floor using a Gabor regression model. The problem is formulated in a joint Bayesian framework and we use a Gibbs MCMC sampler to estimate parameters. We show how to deal with variable magnitude impulses using a shifted inverse gamma distribution for their variance. Our results show improved signal to noise ratios and perceived audio quality by explicitly modelling impulses with a discrete switching process and a new heavy-tailed amplitude model.
Keywords :
Gaussian noise; audio signal processing; gamma distribution; regression analysis; signal denoising; Gabor regression model; Gaussian noise floor; Gibbs MCMC sampler; audio quality; background noise removal; discrete switching process; heavy-tailed amplitude model; impulse noise removal; nonGaussian impulses; parameter estimation; shifted inverse gamma distribution; signal-to-noise ratios; Bayesian methods; Markov processes; Mathematical model; Noise measurement; Noise reduction; Signal to noise ratio; Gabor; Impulse; MCMC; Noise removal;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946390