Data-Driven Sound Track Generation

Authors Meinard Müller, Jonathan Driedger

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Meinard Müller
Jonathan Driedger

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Meinard Müller and Jonathan Driedger. Data-Driven Sound Track Generation. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 175-194, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


Background music is often used to generate a specific atmosphere or to draw our attention to specific events. For example in movies or computer games it is often the accompanying music that conveys the emotional state of a scene and plays an important role for immersing the viewer or player into the virtual environment. In view of home-made videos, slide shows, and other consumer-generated visual media streams, there is a need for computer-assisted tools that allow users to generate aesthetically appealing music tracks in an easy and intuitive way. In this contribution, we consider a data-driven scenario where the musical raw material is given in form of a database containing a variety of audio recordings. Then, for a given visual media stream, the task consists in identifying, manipulating, overlaying, concatenating, and blending suitable music clips to generate a music stream that satisfies certain constraints imposed by the visual data stream and by user specifications. It is our main goal to give an overview of various content-based music processing and retrieval techniques that become important in data-driven sound track generation. In particular, we sketch a general pipeline that highlights how the various techniques act together and come into play when generating musically plausible transitions between subsequent music clips.
  • Sound track
  • content-based retrieval
  • audio matching
  • time-scale modification
  • warping
  • tempo
  • beat tracking
  • harmony


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