Machine Learning

Similarity graphs for the concealment of long duration data loss in music

Tagged: ,

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 1 year, 2 months ago.


  • arXiv
    5 pts

    Similarity graphs for the concealment of long duration data loss in music

    We present a novel method for the compensation of long duration data gaps 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. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.

    Similarity graphs for the concealment of long duration data loss in music
    by Nathanael Perraudin, Nicki Holighaus, Piotr Majdak, Peter Balazs
    https://arxiv.org/pdf/1607.06667v3.pdf

You must be logged in to reply to this topic.