Subject: [AUDITORY] DEBUSSY: open-source Python tool for standardised acoustic reporting of arousal stimuli From: Hyeonjoong Kim <hyeonjoongk@xxxxxxxx> Date: Tue, 11 Aug 2026 08:00:00 +0900--000000000000de97f60658b95339 Content-Type: text/plain; charset="UTF-8" Dear list, I would like to share an open-source tool that may be useful to anyone preparing auditory stimuli for autonomic or psychophysiological experiments. The problem. Studies on sound and autonomic arousal - sleep, anxiety, cardiac vagal tone, biofeedback - routinely report a custom subset of level, temporal, spectral and psychoacoustic descriptors. Because each lab chooses its own window lengths, A-weighting timebase, onset detector and library versions, the resulting tables are not comparable across studies even when they nominally report the same parameter. DEBUSSY implements an eleven-item minimum acoustic reporting guideline in one call, with the analysis choices fixed and documented rather than left to the caller: pip install debussy-audio from debussy import analyze_audio r = analyze_audio("stimulus.wav") r.laeq_dbfs_a, r.roughness_asper, r.sharpness_acum r.to_json() It composes librosa for time-frequency descriptors with MOSQITO for the ISO/DIN psychoacoustic models (Zwicker loudness, DIN 45692 sharpness, Daniel-Weber roughness), and adds a small number of internally implemented metrics. Every parameter carries an evidence tier, which separates "is this stimulus admissible?" from "what does this stimulus do?". Validation. A 60-track benchmark spanning DEAM low-arousal, DEAM mid-to-high plus a genre-stratified FMA-medium subset, and a set of breath-paced clinical stimuli. The manifest, the raw per-track parameter matrix and the scripts that regenerate the figures and statistics are all published, so the numbers can be checked without the audio. The between-music-category contrast is explicitly underpowered and reported as such; the benchmark's purpose is to show every parameter returns a sensible, bounded distribution on very different material. Source: https://github.com/hyeonjoong/debussy Docs: https://hyeonjoong.github.io/debussy Demo (no install): https://huggingface.co/spaces/jjjooong/debussy MIT licensed, Python 3.10-3.12, tested on Linux and macOS The reporting guideline comes from a narrative review currently under review at Neuroscience and Biobehavioral Reviews; a software paper is in preparation for JOSS. I would genuinely value criticism of the parameter choices and the fixed defaults, particularly from anyone who has fought the same comparability problem. Issues and pull requests are welcome. Disclosure: I am research director at Bell Therapeutics, which develops sound-based sleep interventions. One of the three validation categories is our clinical stimulus set. The tool itself is independent and MIT licensed. Hyeon-Joong Kim NeuroTech Research Institute, Bell Therapeutics, Seoul ORCID 0000-0002-2898-0464 *Hyeon-Joong Aiden Kim, DVM, PhD.* *Postdoctoral Researcher* *University of North Carolina at Chapel Hill* *School of Medicine, Department of Neurology* *Mobile: +01 919-888-7782* *E-mail : hyeonjoongk@xxxxxxxx <hyeonjoongk@xxxxxxxx>* --000000000000de97f60658b95339 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable <div dir=3D"ltr"><div>Dear list,<br><br>I would like to share an open-sourc= e tool that may be useful to anyone<br>preparing auditory stimuli for auton= omic or psychophysiological experiments.<br><br>The problem. Studies on sou= nd and autonomic arousal - sleep, anxiety,<br>cardiac vagal tone, biofeedba= ck - routinely report a custom subset of level,<br>temporal, spectral and p= sychoacoustic descriptors. Because each lab chooses<br>its own window lengt= hs, A-weighting timebase, onset detector and library<br>versions, the resul= ting tables are not comparable across studies even when<br>they nominally r= eport the same parameter.<br><br>DEBUSSY implements an eleven-item minimum = acoustic reporting guideline in<br>one call, with the analysis choices fixe= d and documented rather than left to<br>the caller:<br><br>=C2=A0 =C2=A0 pi= p install debussy-audio<br><br>=C2=A0 =C2=A0 from debussy import analyze_au= dio<br>=C2=A0 =C2=A0 r =3D analyze_audio("stimulus.wav")<br>=C2= =A0 =C2=A0 r.laeq_dbfs_a, r.roughness_asper, r.sharpness_acum<br>=C2=A0 =C2= =A0 r.to_json()<br><br>It composes librosa for time-frequency descriptors w= ith MOSQITO for the<br>ISO/DIN psychoacoustic models (Zwicker loudness, DIN= 45692 sharpness,<br>Daniel-Weber roughness), and adds a small number of in= ternally implemented<br>metrics. Every parameter carries an evidence tier, = which separates "is this<br>stimulus admissible?" from "what= does this stimulus do?".<br><br>Validation. A 60-track benchmark span= ning DEAM low-arousal, DEAM mid-to-high<br>plus a genre-stratified FMA-medi= um subset, and a set of breath-paced<br>clinical stimuli. The manifest, the= raw per-track parameter matrix and the<br>scripts that regenerate the figu= res and statistics are all published, so the<br>numbers can be checked with= out the audio. The between-music-category<br>contrast is explicitly underpo= wered and reported as such; the benchmark's<br>purpose is to show every= parameter returns a sensible, bounded distribution<br>on very different ma= terial.<br><br>=C2=A0 Source: =C2=A0<a href=3D"https://github.com/hyeonjoon= g/debussy">https://github.com/hyeonjoong/debussy</a><br>=C2=A0 Docs: =C2=A0= =C2=A0<a href=3D"https://hyeonjoong.github.io/debussy">https://hyeonjoong.= github.io/debussy</a><br>=C2=A0 Demo (no install): <a href=3D"https://huggi= ngface.co/spaces/jjjooong/debussy">https://huggingface.co/spaces/jjjooong/d= ebussy</a><br>=C2=A0 MIT licensed, Python 3.10-3.12, tested on Linux and ma= cOS<br><br>The reporting guideline comes from a narrative review currently = under review<br>at Neuroscience and Biobehavioral Reviews; a software paper= is in<br>preparation for JOSS.<br><br>I would genuinely value criticism of= the parameter choices and the fixed<br>defaults, particularly from anyone = who has fought the same comparability<br>problem. Issues and pull requests = are welcome.<br><br>Disclosure: I am research director at Bell Therapeutics= , which develops<br>sound-based sleep interventions. One of the three valid= ation categories is<br>our clinical stimulus set. The tool itself is indepe= ndent and MIT licensed.<br><br>Hyeon-Joong Kim<br>NeuroTech Research Instit= ute, Bell Therapeutics, Seoul<br>ORCID 0000-0002-2898-0464</div><div><div d= ir=3D"ltr" class=3D"gmail_signature" data-smartmail=3D"gmail_signature"><di= v dir=3D"ltr"><div><div dir=3D"ltr"><div><div dir=3D"ltr"><div style=3D"fon= t-family:undefined,sans-serif;font-size:14px;background-color:rgb(255,255,2= 55)"><font color=3D"#999999"><i><b>Hyeon-Joong Aiden Kim, DVM, PhD.</b></i>= </font></div><div style=3D"font-family:undefined,sans-serif;font-size:14px;= background-color:rgb(255,255,255)"><font color=3D"#999999"><i>Postdoctoral = Researcher</i></font></div><div style=3D"font-family:undefined,sans-serif;f= ont-size:14px;background-color:rgb(255,255,255)"><font color=3D"#999999"><i= >University of North Carolina at Chapel Hill</i></font></div><div style=3D"= font-family:undefined,sans-serif;font-size:14px;background-color:rgb(255,25= 5,255)"><font color=3D"#999999"><i>School of Medicine, Department of Neurol= ogy</i></font></div><div style=3D"font-family:undefined,sans-serif;font-siz= e:14px;background-color:rgb(255,255,255)"><font color=3D"#999999"><i>Mobile= : +01 919-888-7782</i></font></div><div style=3D"font-family:undefined,sans= -serif;font-size:14px;background-color:rgb(255,255,255)"><font color=3D"#99= 9999"><i>E-mail : <a href=3D"mailto:hyeonjoongk@xxxxxxxx" target=3D"_blank= ">hyeonjoongk@xxxxxxxx</a></i></font></div></div></div></div></div></div><= /div></div></div> --000000000000de97f60658b95339--