technical paper

AAAI 2024

Vancouver , Canada

MID-FiLD: MIDI Dataset for Fine-Level Dynamics | VIDEO


app: other applications

app: art/music/creativity

ml: applications

One of the challenges in generating human-like music is articulating musical expressions such as dynamics, phrasing, and timbre, which are difficult for computational models to mimic. Previous efforts to tackle this problem have been insufficient due to a fundamental lack of data containing information about musical expressions. In this paper, we introduce MID-FiLD, a MIDI dataset for learning fine-level dynamics control. Notable properties of MID-FiLD are as follows: (1) All 4,422 MIDI samples are constructed by professional music writers with a strong understanding of composition and musical expression. (2) Each MIDI sample contains four different musical metadata including control change #1 (CC#1) value. We verify that our metadata is a key factor in MID-FiLD, exerting a substantial influence over CC#1 values. In addition, we demonstrate the applicability of MID-FiLD to deep learning models by suggesting a token-based encoding methodology and reveal the potential for generating controllable, human-like musical expressions.


SlidesPaperTranscript English (automatic)

Next from AAAI 2024

Learning to Rank in Generative Retrieval
technical paper

Learning to Rank in Generative Retrieval

AAAI 2024

+2Liang WangYongqi Li
Yongqi Li and 4 other authors

Stay up to date with the latest Underline news!

Select topic of interest (you can select more than one)


  • All Lectures
  • For Librarians
  • Resource Center
  • Free Trial
Underline Science, Inc.
1216 Broadway, 2nd Floor, New York, NY 10001, USA

© 2023 Underline - All rights reserved