Inpainting of long audio segments with similarity graphs
This addresses audio inpainting for music restoration, but it appears incremental as it builds on existing graph and optimization techniques.
The paper tackled the problem of compensating for long-duration data loss in audio signals, particularly music, by using a graph-based method to find and insert replacement segments, with listening tests showing highly promising results.
We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e. the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.