NDLI: Translating Melody to Chord: Structured and Flexible Harmonization of Melody With Transformer

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Seungyeon Rhyu Hyeonseok Choi Sarah Kim Kyogu Lee
Abstract	Recent deep learning approaches for melody harmonization have achieved remarkable performance by overcoming the uneven chord distributions of music data. However, most of these approaches have not attempted to capture an original melodic structure and generate structured chord sequences with appropriate rhythms. Hence, we use a Transformer-based architecture that directly maps lower-level melody notes into a semantic higher-level chord sequence. In particular, we encode the binary piano roll of a melody into a note-based representation. Furthermore, we address the flexible generation of various chords with Transformer expanded with a VAE framework. We propose three Transformer-based melody harmonization models: 1) the standard Transformer-based model for the neural translation of a melody to chords (STHarm); 2) the variational Transformer-based model for learning the global representation of complete music (VTHarm); and 3) the regularized variational Transformer-based model for the controllable generation of chords (rVTHarm). Experimental results demonstrate that the proposed models generate more structured, diverse chord sequences than LSTM-based models.
e-ISSN	21693536
DOI	10.1109/ACCESS.2022.3155467
Journal	IEEE Access
Volume Number	10
Language	English
Publisher	IEEE
Publisher Date	2022-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Music Information Retrieval Computer Generated Music Neural Networks Self-supervised Learning
Content Type	Text
Resource Type	Article

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