Bayesian Restoration of Audio Degraded by Low-Frequency Pulses Modeled via Gaussian Process
This work addresses audio restoration for old vinyl and gramophone recordings, offering an incremental improvement by automating pulse suppression with reduced manual input.
The paper tackles the problem of removing low-frequency pulses from old audio recordings by introducing a Bayesian method that jointly estimates pulse location, interpolates the underlying signal, and models the pulse tail using a Gaussian Process, achieving perceptual results similar to previous approaches with less user intervention.
A common defect found when reproducing old vinyl and gramophone recordings with mechanical devices are the long pulses with significant low-frequency content caused by the interaction of the arm-needle system with deep scratches or even breakages on the media surface. Previous approaches to their suppression on digital counterparts of the recordings depend on a prior estimation of the pulse location, usually performed via heuristic methods. This paper proposes a novel Bayesian approach capable of jointly estimating the pulse location; interpolating the almost annihilated signal underlying the strong discontinuity that initiates the pulse; and also estimating the long pulse tail by a simple Gaussian Process, allowing its suppression from the corrupted signal. The posterior distribution for the model parameters as well for the pulse is explored via Markov-Chain Monte Carlo (MCMC) algorithms. Controlled experiments indicate that the proposed method, while requiring significantly less user intervention, achieves perceptual results similar to those of previous approaches and performs well when dealing with naturally degraded signals.