Interpolation of missing data values for audio signal restoration using a Gabor regression model

We present a method to address the (inherently ill-posed) problem of missing data interpolation over repeated short gaps in audio signals. By formulating the problem in terms of a Gabor regression model, we show that it is possible to leverage information from the surrounding time-frequency plane in order to obtain an interpolation in keeping with the qualities of the signal under consideration. As an exploratory investigation of this technique's potential, we consider two example restoration scenarios in which over one third of the data values in total are missing.