Biotuner: A python toolbox integrating music theory and signal processing for harmonic analysis of physiological and natural time series
Bibliographic record
Abstract
BACKGROUND: The Biotuner Toolbox is an open-source Python toolbox for biosignals that integrates concepts from neuroscience, music theory, and signal processing. It introduces a harmonic perspective on physiological oscillations by applying musical constructs such as consonance, rhythm, and scale construction. METHODS: The core biotuner_object processes neural, cardiac, and auditory time series, providing a unified interface for extracting spectral peaks, computing harmonicity metrics, and supporting downstream analyses. Companion modules extend harmonic analyses across temporal (time-resolved harmonicity), spatial (harmonic connectivity), and spectral (harmonic spectrum) dimensions. RESULTS: Biotuner identifies harmonic structure across different biosignals, revealing significant variations in harmonicity between physiological states. Specifically, the toolbox extracts spectral peaks from complex signals using multiple algorithms, ensuring robust peak detection under varying signal-to-noise ratios. Moreover, we show how harmonicity metrics change across distinct sleep stages and capture variations in the slopes of the aperiodic (1/f) component of the power spectrum. CONCLUSION: Biotuner provides an extensible framework that unifies music-theoretic constructs with biosignal processing, enabling hypothesis-driven analyses for researchers and, in parallel, creative exploration of complex natural patterns for artists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.024 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".