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Modeling the rhythmic complexity of professional drumming with an oscillation-driven reservoir computer.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Oscillation-driven reservoir computing ↔ ODRC_drum/param_ODRC.m, lines 4–25 · score 0.67 · firing rate, neural units, recursive, squares, feedback, connected
  2. [2] § Methods › Encoding and decoding of drum performances ↔ ODRC_drum/midi_code.m, the whole file · a weak match · score 0.59 · cymbals, snares, toms, instrument, hi hat, MIDI

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 54 lines · 1.2 KB · no license · 1 match

  1. %% ODRC (oscillation-driven reservoir computing) for Learning Drumming Performances
  2. %% main parameters
  4. %% recurrent neural network
  5. numUnits = 5000; % number of neural units
  6. p_connect = 0.1; % connection probability
  7. g = 1.2; % synaptic strength scaling factor
  8. numIn = 1; % number of input units
  10. % input
  11. input_weight_amp = 5.0;
  12. input_pulse_value = 2.0;
  13. feedback_weight_amp = 3.0;
  14. Osc_weight_amp = 0.5;
  16. % oscillator
  17. numOsc = 10; % number of oscillators
  18. fmin = 50; % minimum frequency (Hz)
  19. fmax = 100; % maximum frequency (Hz)
  21. % firing rate model
  22. tau = 10.0; % time constant
  24. % recursive least squares
  25. delta = 10; % P matrix initialization
  28. %% task
  29. length_train = 20000; % training duration (ms)
  30. length_test = 40000; % test duration (ms)
  32. % number of loops
  33. learn_every = 2; % skip time points
  34. n_train_loops = 2; % number of training loops
  35. n_test_loops = 1; % number of test loops
  37. if strcmp(TARGET, 'hi-hat')
  38. numOut = 1; % number of output units
  39. elseif strcmp(TARGET, 'funk')
  40. numOut = 3;
  41. elseif strcmp(TARGET, 'jazz')
  42. numOut = 5;
  43. elseif strcmp(TARGET, 'samba')
  44. numOut = 5;
  45. elseif strcmp(TARGET, 'rock')
  46. numOut = 5;
  47. else
  48. error('Invalid target name.')
  49. end
  52. %% drawing
  53. lwidth = 1;
  54. fsize = 10;

param_ODRC.m at commit 2f48dc9, no license · at the source

Overview

Authors: Yuji Kawai1, Shinya Fujii2, Minoru Asada1,3,4
ORCID iDs: Yuji Kawai
  1. Symbiotic Intelligent Systems Research Center, Institute for Open and Transdisciplinary Research Initiatives, the University of Osaka, 1-1 Yamadaoka, Suita, Osaka 565-0871 Japan
  2. Faculty of Environment and Information Studies, Keio University, 5322 Endo, Fujisawa, Kanagawa 252-0882 Japan
  3. International Professional University of Technology in Osaka, 3-3-1 Umeda, Kita-ku, Osaka, 530-0001 Japan
  4. Academy of Emerging Sciences, Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi 487-8501 Japan
Institutions: The University of Osaka (Japan); Keio University (Japan); Chubu University (Japan)
Journal: Cognitive neurodynamics, volume 20, issue 1, article 140
Dates: received 18 February 2026; accepted 6 July 2026; published online 16 July 2026; in print December 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s11571-026-10512-5 · PMID 42465670 · PMCID PMC13373152 · OpenAlex W7168410071
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: systems (subfield)
Methods: Preprocessing, Spectral & time-frequency, Complexity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Reservoir computing, Echo state networks, Microtiming, Rhythm generation, Drumming
Topic: Neural Networks and Reservoir Computing (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Osaka University; Japan Society for the Promotion of Science (JP24H02199, 23K25750); Precursory Research for Embryonic Science and Technology (JPMJPR23S9, JPMJPR23S5)
Citations: not cited yet (Europe PMC); 112 references in the paper

Abstract

Musical performances, particularly in drumming, are characterized not only by their structured rhythmic patterns but also by the subtle variations in timing and amplitude series that create expressive complexity. This study proposes a neural-inspired computational model to investigate how the brain might learn and internalize such complex rhythms. Inspired by the established roles of the cerebellum and basal ganglia in production of rhythms and timings, we utilize an oscillation-driven reservoir computer, a recurrent neural network model for temporal learning, to simulate the generation of human-like expressive drumming performances. First, the model was trained to replicate Jeff Porcaro’s distinctive hi-hat patterns. Analyses revealed that the outputs of the model incorporating high-frequency oscillators ([50, 100] Hz), closely matched the original drumming, reproducing its characteristic fluctuations and patterns in inter-beat timings (microtiming) and amplitudes. Next, the model was trained to generate multidimensional drum kit performances for various genres (funk, jazz, samba, and rock). The model’s outputs exhibited timing deviation and audio features characteristic of the original performances. Our findings demonstrate that oscillation-driven reservoir computing can replicate the rhythmic complexity of professional drumming, suggesting it as a potential computational principle for motor timing and rhythm generation. This approach provides a powerful framework for understanding how the brain generates and processes intricate rhythmic patterns.

Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-026-10512-5.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

magenta.withgoogle.com/datasets/groove

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: magenta.withgoogle.com/datasets/groove

Kawai-Yuji/ODRC_drum

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2f48dc93ac1751f42fe3f342198e5e837887181d, 19 January 2026
Languages: MATLAB (13)
Size: 19 files, 13 scripts
Software Heritage: not archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/Kawai-Yuji/ODRC_drum

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The data and code that support the findings of this study are openly available. The hi-hat timing data are derived from a publicly available dataset by Rasanen et al. (2015), accessible at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127902. The Groove MIDI Dataset, as described in Gillick et al. (2019), is available at https://magenta.withgoogle.com/datasets/groove. The source code for our model is available on GitHub at https://github.com/Kawai-Yuji/ODRC_drum.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Funding: added Osaka University; Japan Society for the Promotion of Science: JP24H02199, 23K25750; Precursory Research for Embryonic Science and Technology: JPMJPR23S9, JPMJPR23S5

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 74 references.

Cite

This paper

Kawai, Y., Fujii, S., & Asada, M. (2026). Modeling the rhythmic complexity of professional drumming with an oscillation-driven reservoir computer. Cognitive neurodynamics, 20(1), 140. https://doi.org/10.1007/s11571-026-10512-5

BibTeX

@article{kawai2026modeling,
author = {Kawai, Yuji and Fujii, Shinya and Asada, Minoru},
title = {{Modeling the rhythmic complexity of professional drumming with an oscillation-driven reservoir computer}},
journal = {Cognitive neurodynamics},
year = {2026},
month = jul,
volume = {20},
number = {1},
pages = {140},
publisher = {Springer},
issn = {1871-4080},
doi = {10.1007/s11571-026-10512-5},
url = {https://doi.org/10.1007/s11571-026-10512-5},
pmid = {42465670},
pmcid = {PMC13373152}
}

RIS

TY - JOUR
AU - Kawai, Yuji
AU - Fujii, Shinya
AU - Asada, Minoru
TI - Modeling the rhythmic complexity of professional drumming with an oscillation-driven reservoir computer
T2 - Cognitive neurodynamics
J2 - Cogn Neurodyn
PY - 2026
DA - 2026/07/16
VL - 20
IS - 1
SP - 140
SN - 1871-4080
PB - Springer
DO - 10.1007/s11571-026-10512-5
UR - https://doi.org/10.1007/s11571-026-10512-5
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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