Calliope -- A Polyphonic Music Transformer
Not on the map. Why?
Abstract (arXiv)
The polyphonic nature of music makes the application of deep learning to music modelling a challenging task. On the other hand, the Transformer architecture seems to be a good fit for this kind of data. In this work, we present Calliope, a novel autoencoder model based on Transformers for the efficient modelling of multi-track sequences of polyphonic music. The experiments show that our model is able to improve the state of the art on musical sequence reconstruction and generation, with remarkably good results especially on long sequences.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 14 references that are in the map. First posted on arXiv 8 Jul 2021.
Builds on
11 of 14 references.
References · 11
- 2017Attention Is All You Needcited by 59,932
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2013Auto-Encoding Variational Bayescited by 3,980
- 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,830
- 2015Adversarial Autoencoderscited by 359
- 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
- 2018A Hierarchical Latent Vector Model for Learning Long-Term Structure in Musiccited by 131
- 2017Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networkscited by 107
- 2019Scheduled Sampling for Transformerscited by 46
- 2019Encoding Musical Style with Transformer Autoencoderscited by 35
- 2020Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoderscited by 4
Built on by
Citing papers per year: 2023: 1.
1 paper.
- 2023Graph-based Polyphonic Multitrack Music Generationcited by 2
- 2023Graph-based Polyphonic Multitrack Music Generation27 Jul 2023