2025/09/21 by Goswami, Mandip
Engineering · Health Professions · #Acoustic Wave Phenomena Research #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Noise Effects and Management #Sound (cs.SD) #Vehicle Noise and Vibration Control #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2509.16901
openalex publication_date 2025/09/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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).