NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization (2402.17907v1)

Abstract: Head-related transfer functions (HRTFs) are important for immersive audio, and their spatial interpolation has been studied to upsample finite measurements. Recently, neural fields (NFs) which map from sound source direction to HRTF have gained attention. Existing NF-based methods focused on estimating the magnitude of the HRTF from a given sound source direction, and the magnitude is converted to a finite impulse response (FIR) filter. We propose the neural infinite impulse response filter field (NIIRF) method that instead estimates the coefficients of cascaded IIR filters. IIR filters mimic the modal nature of HRTFs, thus needing fewer coefficients to approximate them well compared to FIR filters. We find that our method can match the performance of existing NF-based methods on multiple datasets, even outperforming them when measurements are sparse. We also explore approaches to personalize the NF to a subject and experimentally find low-rank adaptation to be effective.

• The paper introduces NIIRF that directly estimates cascaded IIR filter parameters for efficient HRTF upsampling and enhanced personalization.
• It demonstrates superior upsampling performance and reduced computational complexity, especially with sparse HRTF measurements.
• The study employs low-rank adaptation for personalization, paving the way for realistic spatial audio rendering in immersive applications.

Neural IIR Filter Field: A Novel Approach for High-Quality HRTF Upsampling and Personalization

Overview of the Proposed Method

The paper introduces a neural infinite impulse response filter field (NIIRF) for head-related transfer function (HRTF) modeling, addressing the challenges of spatial upsampling and personalization. Traditional approaches to HRTF modeling, such as vector-based amplitude panning and spatial decomposition, are limited by computational complexity and the underdetermined nature of spatial coefficients estimation. Recently, neural field (NF) methods estimating HRTF magnitudes have demonstrated promising results; however, these require conversions to time-domain FIR filters, introducing challenges in terms of coefficient volume and fidelity when measurements are sparse.

The proposed NIIRF framework exploits the elegant properties of IIR filters, which can mimic HRTFs with fewer parameters compared to FIR filters, thus offering a computationally efficient alternative. Through an integration of NF-based spatial upsampling with cascaded differentiable IIR filters, the NIIRF method estimates the parameters of IIR filters directly, leveraging the back-propagation for optimization. This framework not only improves HRTF upsampling accuracy, especially in scenarios with limited measurements but also facilitates effective personalization to new subjects via low-rank adaptation and other conditioning approaches.

Key Contributions and Findings

• Neural IIR Filter Field (NIIRF) Design: The paper proposes an innovative design that directly estimates the parameters of cascaded IIR filters for HRTF modeling. By optimizing these parameters through a differentiable signal processing approach, the work significantly reduces the computational complexity and memory footprint, presenting a more efficient modeling technique.
• Superior Upsampling Performance: In comparative analysis employing multiple datasets, NIIRF matches and sometimes surpasses the performance of current NF-based methods, particularly in settings where HRTF measurements are sparse.
• Effective Personalization Strategies: The investigation into personalizing NF to specific subjects reveals the effectiveness of low-rank adaptation, offering insights into efficient techniques for adapting pre-trained models to new individuals with limited data.
• Empirical Validation: The experimental results, including comparisons with classical and NF-based baselines, validate the proposed method's improvements over existing approaches.

Theoretical and Practical Implications

The introduction of NIIRF marks a significant advancement in HRTF modeling, both from theoretical and practical perspectives. Theoretically, it underscores the viability and benefits of IIR filters in capturing the modal nature of HRTFs, paving the way for future research into more efficient and accurate spatial audio modeling techniques. Practically, the ability to perform high-quality HRTF upsampling with fewer measurements and efficiently personalize the NF to individual subjects can greatly enhance the realism and immersiveness of audio experiences in virtual reality, telepresence, and other spatial audio applications.

Future Directions

While the proposed NIIRF method offers compelling advantages, the exploration of further optimizations and applications remains a promising avenue for future work. Enhancements in the architecture to better capture the non-linear characteristics of HRTFs, along with investigations into the integration of interaural time difference modeling within the current framework, would be valuable extensions. Additionally, the adaptability of NIIRF to dynamic or real-time HRTF estimation scenarios warrants investigation, potentially expanding its utility across a wider range of spatial audio technologies.

Conclusion

The paper presents the novel NIIRF method, achieving notable success in HRTF upsampling and personalization through the utilization of cascaded IIR filters and neural field techniques. This method not only demonstrates superior performance compared to existing approaches but also introduces a more computationally efficient pathway for future developments in spatial audio modeling. The findings and methodologies outlined in this work set a new benchmark for the research community and offer practical insights for advancing the immersive audio technologies crucial to the next generation of multimedia experiences.