Greek folk music denoising under a symmetric α-stable noise assumption
Heterogeneous Networking for Quality, Reliability, Security and Robustness(2014)
摘要
The noise in musical audio recordings is assumed to obey an α-stable distribution. A sparse linear regression framework with structured priors is elaborated. Markov Chain Monte Carlo is used to infer the clean music signal model and the α-stable noise distribution parameters. The musical audio recordings are processed both as a whole and in segments by using a sine-bell window for analysis and overlap-and-add reconstruction. Experiments on noisy Greek folk music excerpts demonstrate better denoising under the α-stable noise assumption than the Gaussian white noise one, and when processing is performed in segments rather than in full recordings.
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关键词
AWGN,Markov processes,Monte Carlo methods,audio recording,audio signal processing,music,regression analysis,signal denoising,α-stable noise assumption,α-stable noise distribution parameter,Gaussian white noise,Greek folk music audio denoising,Markov chain Monte Carlo framework,music signal model,overlap-and-add reconstruction,sine-bell window,sparse linear regression framework
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