INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin

2025-10-08

AI Summary

To address key bottlenecks in acoustic modeling for enclosed spaces such as automotive cabins—including manual hyperparameter tuning, strong hardware dependency, poor adaptability to dynamic environments (e.g., passenger presence, seat adjustments), and frequency-selective responses—this paper proposes an implicit neural representation method for modeling complex-valued frequency response fields. Our approach employs an end-to-end frequency-domain forward model that jointly encodes source/receiver positions and orientations, augmented by auditory- and hardware-aware spectral supervision and physics-informed Kramers–Kronig consistency constraints. These components collectively enhance model robustness and physical interpretability. Evaluated on real in-vehicle measurements, our method reduces magnitude and phase reconstruction errors by 39% and 51%, respectively, outperforming both time-domain and hybrid-domain baselines. It establishes a new paradigm for dynamic, high-fidelity, and physically consistent in-cabin acoustic modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Generative Models & Autoencoders
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate modeling of spatial acoustics is critical for immersive and intelligible audio in confined, resonant environments such as car cabins. 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

Problem

• Modeling spatial acoustics in confined resonant car cabins
• Overcoming manual static hardware-intensive audio tuning methods
• Addressing frequency selective behaviors and dynamic environmental changes

Innovation

• Learns frequency response fields in 3D space
• Uses spectral supervision for critical auditory bands
• Applies physics-based Kramers-Kronig consistency constraint