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Musicians and nonmusicians alike use rhythmic sound gestures, such as tapping and beatboxing, to express drum patterns. While these gestures effectively communicate musical ideas, realizing these ideas as fully-produced drum recordings can be time-consuming, potentially disrupting many creative workflows. To bridge this gap, we present TRIA (The Rhythm In Anything), a masked transformer model for mapping rhythmic sound gestures to high-fidelity drum recordings. Given an audio prompt of the desired rhythmic pattern and a second prompt to represent drum kit timbre, TRIA produces audio of a drum kit playing the desired rhythm (with appropriate elaborations) in the desired timbre. Subjective and objective evaluations show that a TRIA model trained on less than 10 hours of publicly-available drum data can generate high-quality, faithful realizations of sound gestures across a wide range of timbres in a zero-shot manner.
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A Computational Exploration of Musical Timbre
This extended abstract describes an investigation into musical timbre and the automatic discrimination of musical instrument sources. Arising from the first author’s undergraduate honors thesis, this research begins with exploration of fundamental concepts before investigating perceptual correlates of timbre. The work described showcases how one might approach the problem of musical instrument classification through computational analysis. It is hoped that this work may lend ideas to others interested in exploring musical timbre, or likewise inspire others to explore a topic of interest in an unfamiliar domain.
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- Award ID(s):
- 2100874
- PAR ID:
- 10594483
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7162-8
- Page Range / eLocation ID:
- 184 to 85
- Format(s):
- Medium: X
- Location:
- Kingston, ON, Canada
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCECE59415.2024.10667275
