INFER : Learning Implicit Neural Frequency Response Fields for Confined Car Cabin
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
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of INFER, a novel frequency-domain neural framework for modeling complex acoustic environments in confined spaces, which significantly advances the state-of-the-art in neural acoustic modeling. The comprehensive analysis of the technical contributions, innovative methodology, and substantial experimental validation underscores its significance to the field of machine learning and audio processing.
Methodology
The proposed INFER framework introduces a novel end-to-end frequency-domain neural model that learns complex-valued frequency response fields, addressing the limitations of existing acoustic modeling methods. The methodology is well-grounded in physical principles, utilizing Kramers-Kronig relations to ensure causality and consistency between amplitude and phase responses. The incorporation of perceptual and hardware-aware spectral supervision is a significant advancement, allowing the model to prioritize critical auditory frequency bands while downweighting less stable regions. The approach's reliance on implicit neural representations (INRs) to model acoustic fields in confined spaces is innovative, particularly in its ability to capture frequency-dependent behaviors and dynamic changes.
Results & evaluation
The experiments are robust, involving both simulated and real-world datasets collected from various car cabins. The evaluation metrics are comprehensive, covering both magnitude and phase reconstruction errors, and the results demonstrate significant improvements over state-of-the-art methods. The paper provides clear quantitative results, showing reductions in reconstruction errors by over 39% and 51%, respectively. Qualitative assessments further validate the model's performance, showcasing its ability to accurately reproduce complex acoustic phenomena in confined spaces.
Reproducibility
The paper includes detailed implementation information, including model architecture, training procedures, and data collection methods. However, the absence of a publicly available code repository or demo URL limits the reproducibility of the results. Clear guidelines for replicating the experiments would enhance the paper's impact.
Limitations
While the proposed method shows promise, it may face challenges in generalizing to highly variable acoustic environments beyond car cabins. The reliance on specific hardware configurations for data collection might also limit the applicability of the findings. Additionally, the complexity of the model may pose challenges in real-time applications, which are critical for automotive audio systems.
Broader impact
The INFER framework has significant implications for the automotive audio industry, potentially enhancing the quality of in-vehicle audio experiences. Its applications extend to adaptive noise cancellation, spatial audio rendering, and personalized audio experiences, which are increasingly relevant in modern vehicles. The methodology could also inspire further research in acoustic modeling for other confined environments, such as theaters or small auditoriums. The main contribution of this paper is the introduction of INFER, a novel frequency-domain neural framework for modeling complex acoustic environments in confined spaces, which significantly advances the state-of-the-art in neural acoustic modeling. The comprehensive analysis of the technical contributions, innovative methodology, and substantial experimental validation underscores its significance to the field of machine learning and audio processing.
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