Perceptually Guided Optimization Cuts Computational Costs for Personalized Spatial Audio Models

A perceptually guided optimization method designed for deep-learning-based Head-Related Transfer Function personalization achieves a 30% to 52% reduction in computational costs without causing a statistically significant loss in prediction accuracy. Personalization of Head-Related Transfer Functions is necessary for the perceptually accurate rendering of spatial audio across immersive multimedia applications. By incorporating the perceptual sensitivity of the human auditory system directly into the optimization framework, the newly proposed approach resolves substantial resource bottlenecks that arise during model training.

Achieving effective personalization of Head-Related Transfer Functions involves establishing a complex, high-dimensional mapping between the unique anthropometric features of an individual listener and the corresponding transfer function profiles. Because of the high dimensional nature of this acoustic mapping, deep learning frameworks are widely used for this task. However, because deep-learning-based personalization operates within this high-dimensional space, it inevitably leads to exceptionally high demands in terms of computational resources and memory capacity.

These heavy resource demands become a severe bottleneck particularly during the model training phase. Current optimization methods applied to deep-learning-based personalization fail to account for the varying perceptual sensitivity that human hearing exhibits across different spatial regions.

To address this operational issue, the researchers proposed a more efficient and context-specific optimization framework formulated specifically for deep-learning-based personalization models. The method achieves greater efficiency by incorporating the perceptual sensitivity of the human auditory system directly into the mathematical optimization problem. Specifically, the gradient update applied during model training is scaled by a region-specific factor derived from the Minimum Audible Angle.

Experimental evaluations conducted on a publicly available database and implemented across several recent deep-learning Head-Related Transfer Function personalization algorithms confirm that the proposed approach reduces computational costs significantly. In particular, the experimental results demonstrate that the perceptually guided optimization achieves a 30% to 52% reduction in floating-point operations, or FLOPs.

In addition to lowering floating-point operations, the proposed approach achieved a 30% to 52% reduction in runtime and a 30% to 52% reduction in the total number of parameter update steps during training, designated as NENB. Importantly, experimental comparisons verified that these substantial computational and temporal reductions were achieved without a statistically significant loss in prediction accuracy, preserving the fidelity of the personalized spatial audio models.

The proposed framework also exhibits high implementation flexibility, as it can be easily adapted and implemented in different deep-learning-based Head-Related Transfer Function personalization algorithms. This structural adaptability makes the approach highly suitable for efficient model training and ongoing model adaptation across varied deep-learning systems. Consequently, the technique offers a practical pathway for optimizing personalized Head-Related Transfer Functions across diverse immersive audio applications.

Subject of Research: Deep learning optimization for Head-Related Transfer Function personalization

Article Title: Perceptually guided optimization for computationally efficient personalization of HRTF

Article References: Pandagre, B., & Narwaria, M. (2026). Perceptually guided optimization for computationally efficient personalization of HRTF. Multimedia Tools and Applications, 85(11), Article 831. https://doi.org/10.1007/s11042-026-21996-y

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21996-y

Keywords: Head-Related Transfer Functions, Spatial Audio, Deep Learning, Minimum Audible Angle, Auditory Perception, Optimization Algorithms, Immersive Multimedia, Model Training Efficiency, Perceptually, guided, optimization, computationally