Generating Music Medleys via Playing Music Puzzle Games

Authors

  • Yu-Siang Huang Academia Sinica
  • Szu-Yu Chou Academia Sinica
  • Yi-Hsuan Yang Academia Sinica

Keywords:

Music Medley, Music Puzzle, Siamese Network, self-supervised learning

Abstract

Generating music medleys is about finding an optimal permutation of a given set of music clips. Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music. In essence, such a game requires machines to correctly sort a few multisecond music fragments. In the training stage, we learn the model by sampling multiple non-overlapping fragment pairs from the same songs and seeking to predict whether a given pair is consecutive and is in the correct chronological order. For testing, we design a number of puzzle games with different difficulty levels, the most difficult one being music medley, which requiring sorting fragments from different songs. On the basis of state-of-the-art Siamese convolutional network, we propose an improved architecture that learns to embed frame-level similarity scores computed from the input fragment pairs to a common space, where fragment pairs in the correct order can be more easily identified. Our result shows that the resulting model, dubbed as the similarity embedding network (SEN), performs better than competing models across different games, including music jigsaw puzzle, music sequencing, and music medley. Example results can be found at our project website, https://remyhuang.github.io/DJnet.

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Published

2018-04-26

How to Cite

Huang, Y.-S., Chou, S.-Y., & Yang, Y.-H. (2018). Generating Music Medleys via Playing Music Puzzle Games. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11843

Issue

Section

Main Track: Machine Learning Applications