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ISMIR 2019 tutorial: waveform-based music processing with deep learning

Jongpil Lee; Jordi Pons; Sander Dieleman


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3529714", 
  "title": "ISMIR 2019 tutorial: waveform-based music processing with deep learning", 
  "issued": {
    "date-parts": [
      [
        2019, 
        11, 
        4
      ]
    ]
  }, 
  "abstract": "<p>A common practice when processing music signals with deep learning is to transform the raw waveform input into a time-frequency representation. This pre-processing step allows having less variable and more interpretable input signals. However, along that process, one can limit the model&#39;s learning capabilities since potentially useful information (like the phase or high frequencies) is discarded. In order to overcome the potential limitations associated with such pre-processing, researchers have been exploring waveform-level music processing techniques, and many advances have been made with the recent advent of deep learning.</p>\n\n<p>In this tutorial, we introduce three main research areas where waveform-based music processing can have a substantial impact:</p>\n\n<p>1) Classification: waveform-based music classifiers have the potential to simplify production and research pipelines.</p>\n\n<p>2) Source separation: making possible waveform-based music source separation would allow overcoming some historical challenges associated with discarding the phase.</p>\n\n<p>3) Generation: waveform-level music generation would enable, e.g., to directly synthesize expressive music.</p>\n\n<p><a href=\"https://docs.google.com/presentation/d/1_ezZXDkyhp9USAYMc5oKJCkUrUhBfo-Di8H8IfypGBM/edit?usp=sharing\">Link to the original Google Slides</a></p>", 
  "author": [
    {
      "family": "Jongpil Lee"
    }, 
    {
      "family": "Jordi Pons"
    }, 
    {
      "family": "Sander Dieleman"
    }
  ], 
  "type": "speech", 
  "id": "3529714"
}
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