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arXiv · 2509.16901

Automotive Sound Quality for EVs: Psychoacoustic Metrics with Reproducible AI/ML Baselines

Mandip Goswami
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Abstract

We present an open, reproducible reference for automotive sound quality that connects standardized psychoacoustic metrics with lightweight AI/ML baselines, with a specific focus on electric vehicles (EVs). We implement loudness (ISO 532-1/2), tonality (DIN 45681), and modulation-based descriptors (roughness, fluctuation strength), and document assumptions and parameterizations for reliable reuse. For modeling, we provide simple, fully reproducible baselines (logistic regression, random forest, SVM) on synthetic EV-like cases using fixed splits and seeds, reporting accuracy and rank correlations as examples of end-to-end workflows rather than a comparative benchmark. Program-level normalization is reported in LUFS via ITU-R BS.1770, while psychoacoustic analysis uses ISO-532 loudness (sones). All figures and tables are regenerated by scripts with pinned environments; code and minimal audio stimuli are released under permissive licenses to support teaching, replication, and extension to EV-specific noise phenomena (e.g., inverter whine, reduced masking).

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BibTeXRIS

Mandip Goswami. 2025-09-21. Automotive Sound Quality for EVs: Psychoacoustic Metrics with Reproducible AI/ML Baselines. https://arxiv.org/abs/2509.16901

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