DEBUSSY
DEBUSSY implements the eleven-item minimum acoustic reporting guideline for autonomic-arousal stimuli — nine items measured from the audio in a single call, plus two recorded alongside them — with output formats designed for reproducible psychophysiology research.
Statement of need
Researchers preparing auditory stimuli for autonomic-arousal experiments — sleep,
anxiety, cardiac vagal tone, biofeedback — routinely characterise their stimuli
using a custom subset of level, temporal, spectral, and psychoacoustic
descriptors, then report them in tables. Building this characterisation usually
means gluing together at least two Python libraries (librosa for time-frequency
descriptors, MOSQITO for ISO/DIN-aligned psychoacoustic metrics) and writing
several hundred lines of bookkeeping per study. The resulting reports are not
directly comparable across labs because each lab implements the same parameter
with slightly different choices (window length, A-weighting timebase, onset
detector, mosqito version).
DEBUSSY closes this gap with three design choices:
- One call, one schema. A single
analyze_audio()returns a dataclass carrying the eleven reporting items in the order and units of Table 2 of our companion review, so a row can go straight into a manuscript table. - Standardised internals. Window lengths, A-weighting filter coefficients, and the spectral-slope band are fixed and documented.
- Tier-1 / Tier-2 / Tier-3 evaluation built in. Each parameter is tagged with a tier indicating whether it is a universal design check, a directional guideline, or an exploratory descriptor.
The eleven reporting items
These are Table 2 of the companion review — the minimum set proposed for the field. Items 1–8 and 11 are measured from the audio; 9 and 10 are recorded by the caller, since no analyser can determine them.
| # | Item | Unit | Result field(s) |
|---|---|---|---|
| 1 | LAeq + dynamic range | dBFS-A, dB | laeq_dbfs_a, dynamic_range_db |
| 2 | Attack time distribution | ms | attack_mean_ms, attack_median_ms, attack_sd_ms |
| 3 | Roughness | asper | roughness_asper |
| 4 | Tempo / modulation rate | BPM, Hz | tempo_bpm, modulation_peak_hz |
| 5 | Spectral centroid | Hz | spectral_centroid_hz |
| 6 | Sharpness | acum | sharpness_acum |
| 7 | Spectral slope β | — | spectral_slope_beta |
| 8 | Harmonicity / HNR | dB | hnr_db |
| 9 | Lyrics presence | yes/no | lyrics (caller-supplied) |
| 10 | Delivery method | categorical | delivery (caller-supplied) |
| 11 | Spectral flatness | [0, 1] | spectral_flatness |
Result additionally carries crest factor and temporal-coverage descriptors as
diagnostics beyond the guideline.
Note on counts. The companion review evaluates twelve acoustic parameters (its Table 1) and, separately, proposes this eleven-item reporting guideline (its Table 2). The two lists are not the same thing: DEBUSSY implements the reporting guideline.
Install
pip install debussy-audio
To install from source:
git clone https://github.com/hyeonjoong/debussy.git
cd debussy
pip install -e .
Optional extras: pip install debussy-audio[plot] for matplotlib plots,
debussy-audio[test] for the test suite, debussy-audio[demo] for the
Gradio web UI.
Quickstart
from debussy import analyze_audio
result = analyze_audio("stimulus.wav")
print(result)
# Result(file='stimulus.wav', duration_s=45.06, sample_rate=44100,
# laeq_dbfs_a=-17.89, dynamic_range_db=8.1, crest_factor_db=10.93,
# attack_mean_ms=101.59, attack_median_ms=56.11, attack_sd_ms=124.35,
# roughness_asper=0.07, tempo_bpm=132.5, modulation_peak_hz=1.154,
# spectral_centroid_hz=3070.1, sharpness_acum=1.521,
# spectral_slope_beta=-5.368, hnr_db=2.12, spectral_flatness=0.0011, ...)
# Tier framework
from debussy import tier1_compliance, format_compliance
print(format_compliance(result))
Validation
DEBUSSY has been validated on a 60-track open-dataset + clinical benchmark:
- A2 (DEAM low-arousal) n=10
- B1 (DEAM mid-to-high arousal) n=15
- B2 (FMA medium) n=15 (genre-stratified)
- C1 (BELL-001 SleepThera) n=20 (breath-paced biofeedback stimuli)
Fifty-eight of the sixty tracks produce a complete, non-null set of measured
values within physiologically plausible ranges; the two exceptions are ~10 s
breath clips with no autocorrelation peak above the voicing gate, so HNR is
undefined for them.
See paper/ for the JOSS paper, including distribution figures and effect-size
analysis.
The manifest, the raw per-track parameter matrix and the scripts that regenerate
both live in validation/ — analyze_results.py runs on the
published matrix with no audio required.
Citation
If you use DEBUSSY in your research, please cite:
@article{Kim2026,
title = {DEBUSSY: A Python toolbox for the eleven-item minimum acoustic reporting guideline for autonomic-arousal stimuli},
author = {Kim, Hyeon-Joong and others},
year = {2026},
journal = {Journal of Open Source Software},
note = {Manuscript in preparation}
}
Architecture
debussy/
├── level (LAeq, dynamic range, crest factor)
├── envelope (attack time, tempo, modulation peak)
├── spectral (centroid, slope β)
├── tonal (HNR, spectral flatness)
├── psychoacoustic (roughness asper, sharpness acum via MOSQITO)
└── _core (single-call analyze_audio orchestrator + Result dataclass)
The Gradio interface at https://huggingface.co/spaces/jjjooong/debussy is a demo UI built on top of the library; it is not the primary deliverable. The library is the artefact intended for citation and reuse.
Contributing
See CONTRIBUTING.md. Bug reports, feature requests, and pull requests welcome.
License
MIT. See LICENSE.
Acknowledgements
DEBUSSY builds directly on librosa and MOSQITO. We are grateful to both communities.
Metadata
Release files for debussy-audio 0.2.2
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