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 the model, we devise a sequential Markov chain Monte Carlo algorithm (also known as MCMC-Particle filter) to explore the sequence of filtering distributions of the 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.