Theme Transformer

Abstract

Attention-based Transformer models have been increasingly employed for automatic music generation. To condition the generation process of such a model with a user-specified sequence, a popular approach is to take that conditioning sequence as a priming sequence and ask a Transformer decoder to generate a continuation. However, this prompt-based conditioning cannot guarantee that the conditioning sequence would develop or even simply repeat itself in the generated continuation. In this paper, we propose an alternative conditioning approach, called theme-based conditioning, that explicitly trains the Transformer to treat the conditioning sequence as a thematic material that has to manifest itself multiple times in its generation result. This is achieved with two main technical contributions. First, we propose a deep learning-based approach that uses contrastive representation learning and clustering to automatically retrieve thematic materials from music pieces in the training data. Second, we propose a novel gated parallel attention module to be used in a sequence-to-sequence (seq2seq) encoder/decoder architecture to more effectively account for a given conditioning thematic material in the generation process of the Transformer decoder. We report on objective and subjective evaluations of variants of the proposed Theme Transformer and the conventional prompt-based baseline, showing that our best model can generate, to some extent, polyphonic pop piano music with repetition and plausible variations of a given condition.

Demo

Audio Samples

Figures

id	First 24 bars	Melody Embedding Distance
875
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Qualitative results for Theme Retrieval

For more details about Theme Retrieval: link

Comparision for Different Sampling Temperature on Inference phase

File: temperature_comparison.zip

Comparision for Different Eps in DBSCAN

File: eps_comparison.zip

Citation

If you find this work helpful and use our code in your research, please kindly cite our paper:

@article{shih2021theme,
      title={Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer}, 
      author={Yi-Jen Shih and Shih-Lun Wu and Frank Zalkow and Meinard Müller and Yi-Hsuan Yang},
      year={2021},
      eprint={2111.04093},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}