Conference paper Open Access

Supervised Symbolic Music Style Translation Using Synthetic Data

Ondřej Cífka; Umut Simsekli; Gael Richard


Citation Style Language JSON Export

{
  "publisher": "ISMIR", 
  "DOI": "10.5281/zenodo.3527878", 
  "container_title": "Proceedings of the 20th International Society for Music Information Retrieval Conference", 
  "title": "Supervised Symbolic Music Style Translation Using Synthetic Data", 
  "issued": {
    "date-parts": [
      [
        2019, 
        11, 
        4
      ]
    ]
  }, 
  "abstract": "Research on style transfer and domain translation has clearly demonstrated the ability of deep learning-based algorithms to manipulate images in terms of artistic style. More recently, several attempts have been made to extend such approaches to music (both symbolic and audio) in order to enable transforming musical style in a similar manner. In this study, we focus on symbolic music with the goal of altering the 'style' of a piece while keeping its original 'content'. As opposed to the current methods, which are inherently restricted to be unsupervised due to the lack of 'aligned' data (i.e. the same musical piece played in multiple styles), we develop the first fully supervised algorithm for this task. At the core of our approach lies a synthetic data generation scheme which allows us to produce virtually unlimited amounts of aligned data, and hence avoid the above issue. In view of this data generation scheme, we propose an encoder-decoder model for translating symbolic music accompaniments between a number of different styles. Our experiments show that our models, although trained entirely on synthetic data, are capable of producing musically meaningful accompaniments even for real (non-synthetic) MIDI recordings.", 
  "author": [
    {
      "family": "Ond\u0159ej C\u00edfka"
    }, 
    {
      "family": "Umut Simsekli"
    }, 
    {
      "family": "Gael Richard"
    }
  ], 
  "id": "3527878", 
  "event-place": "Delft, The Netherlands", 
  "publisher_place": "Delft, The Netherlands", 
  "type": "paper-conference", 
  "event": "International Society for Music Information Retrieval Conference (ISMIR 2019)", 
  "page": "588-595"
}
552
345
views
downloads
All versions This version
Views 552551
Downloads 345345
Data volume 230.6 MB230.6 MB
Unique views 502501
Unique downloads 311311

Share

Cite as