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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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DRUM: A Real Time Detector for Regime Shifts in Data Streams via an Unsupervised, Multivariate Framework
In this work we present DRUM, an unsupervised approach that is based on statistical properties of multivariate data streams to identify regime shifts in real time. DRUM processes streams in small chunks, learns their statistical properties, and makes generalizations as time goes by. We show how this straightforward approach requires minimal computation and reaches state of the art accuracy, making it ideal for embedded and cyber physical systems.
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- Award ID(s):
- 1757207
- PAR ID:
- 10462095
- Date Published:
- Journal Name:
- Lecture notes in computer science
- ISSN:
- 0302-9743
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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