Title: INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Acoustic scene representation, Implicit representation, spatial acoustics, frequency-domain modeling

Abstract: Accurate modeling of spatial acoustics is critical for immersive and intelligible audio in confined, resonant environments such as car cabin. Current tuning methods are manual, hardware-intensive, and static, failing to account for frequency selective behaviors and dynamic changes like passenger presence or seat adjustments. To address this issue, we propose INFER ( Implicit Neural Frequency Response fields), a frequency-domain neural framework that is jointly conditioned on source and receiver positions, orientations to directly learn complex-valued frequency response fields inside confined, resonant environments like car cabins. We introduce three key innovations over current neural acoustic modeling methods: (1) novel end-to-end frequency-domain forward model that directly learns the frequency response field and frequency-specific attenuation in 3D space; (2) perceptual and hardware-aware spectral supervision that emphasizes critical auditory frequency bands and deemphasizes unstable crossover regions; and (3) a physics-based Kramers–Kronig consistency constraint that regularizes frequency-dependent attenuation and delay. We evaluate our method over real-world data collected in multiple car cabins. Our approach significantly outperforms time- and hybrid-domain baselines on both simulated and real-world automotive datasets, cutting average magnitude and phase reconstruction errors by over 39\% and 51\%, respectively. INFER sets a new state-of-the-art for neural acoustic modeling in automotive spaces.