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.