Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation
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Abstract (arXiv)
This document presents some early explorations of applying Softly Masked Language Modelling (SMLM) to symbolic music generation. SMLM can be seen as a generalisation of masked language modelling (MLM), where instead of each element of the input set being either known or unknown, each element can be known, unknown or partly known. We demonstrate some results of applying SMLM to constrained symbolic music generation using a transformer encoder architecture. Several audio examples are available at https://erl-j.github.io/smlm-web-supplement/
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References · 7
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2016DeepBach: a Steerable Model for Bach Chorales Generationcited by 90
- 2021MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Trainingcited by 89
- 2020MMM : Exploring Conditional Multi-Track Music Generation with the Transformercited by 48
- 2021The Piano Inpainting Applicationcited by 23
- 2021Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encodingcited by 14
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