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        <identifier>oai:drops-oai.dagstuhl.de:3472</identifier>
        <datestamp>2024-03-06T10:27:59Z</datestamp>
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          <dc:title>Data-Driven Sound Track Generation</dc:title>
          <dc:creator>Müller, Meinard</dc:creator>
          <dc:creator>Driedger, Jonathan</dc:creator>
          <dc:subject>Sound track</dc:subject>
          <dc:subject>content-based retrieval</dc:subject>
          <dc:subject>audio matching</dc:subject>
          <dc:subject>time-scale modification</dc:subject>
          <dc:subject>warping</dc:subject>
          <dc:subject>tempo</dc:subject>
          <dc:subject>beat tracking</dc:subject>
          <dc:subject>harmony</dc:subject>
          <dc:description>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.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Meinard Müller and Jonathan Driedger</dc:contributor>
          <dc:date>2012</dc:date>
          <dc:relation>Is Part Of Dagstuhl Follow-Ups, Volume 3, Multimodal Music Processing (2012)</dc:relation>
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          <dc:identifier>doi:10.4230/DFU.Vol3.11041.175</dc:identifier>
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          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.175</dc:identifier>
          <dc:language>eng</dc:language>
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