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 our model, we devise a sequential Markov chain Monte Carlo algorithm to explore the sequence of filtering distributions of 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.