Bayesian Restoration of Audio Degraded by Low-Frequency Pulses Modeled via Gaussian Process

TL;DR

This paper introduces a Bayesian method using Gaussian Processes to automatically detect and suppress low-frequency pulses in degraded audio recordings, improving restoration with less user intervention.

Contribution

It presents a novel Bayesian framework that jointly estimates pulse location, signal interpolation, and pulse tail modeling, advancing audio restoration techniques.

Findings

Achieves perceptually similar results to existing methods

Requires less user intervention

Performs well on naturally degraded signals

Abstract

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.