Symbolic music generation includes various tasks for understanding human-composed or -performed music and mimicking the process for producing them. It particularly aims to computationally model real-world music using symbolic representations, such as Musical Instrument Digital Interface (MIDI). We are interested in several tasks that have been achieved increasing attention in the MIR field.
Automatic Melody Harmonization
1. What is automatic melody harmonization?
Automatic melody harmonization is a task to find coherent chords or an arrangement for the given melody. It has been important for understanding human composition by imitating the harmonizing process of the composers. It can also reduce a barrier to creating music without expertise.
In our lab, we mainly have focused on finding a chord label sequence for the given symbolic melody notes.
2. On-going Research
1) Structured and Flexible Harmonization of Melody with Transformer
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Automatic Performance Generation
1. What is automatic performance generation?
Automatic performance generation focuses on mimicking human behaviors for conveying music from the written score to the actual performance. One of the main objectives of this field is to reproduce the symbolic representations of the expressive attributes, including the loudness or timing of notes, from the given musical pieces.
2. On-going Research
1) Deep Factorization of Expressive Piano Performance with Self-Supervised Learning
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Automatic Music Composition
1. What is automatic music composition?
Automatic music composition focuses on creating music data that is good to listen to or good to reproduce. Generally, in the case of symbolic music generation, MIDI data or sheet music data are generated so that the composer can use them immediately. Furthermore, the combination of symbolic music generation and other producing technology (like Intelligent Music Production) provides "complete" music to listeners.
2. On-going Research
1) FLAGNet: Feature Label based Automatic Generation Network for symbolic music
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