HRTFformer: A Spatially-Aware Transformer for Personalized HRTF Upsampling in Immersive Audio Rendering
Abstract
Personalized Head-Related Transfer Functions (HRTFs) are starting to be introduced in many commercial immersive audio applications and are crucial for realistic spatial audio rendering. However, one of the main hesitations regarding their introduction is that creating personalized HRTFs is impractical at scale due to the complexities of the HRTF measurement process. To mitigate this drawback, HRTF spatial upsampling has been proposed with the aim of reducing measurements required. While prior work has seen success with different machine learning (ML) approaches, these models often struggle with long-range spatial consistency and generalization at high upsampling factors. In this paper, we propose a novel transformer-based architecture for HRTF upsampling, leveraging the attention mechanism to better capture spatial correlations across the HRTF sphere. Working in the spherical harmonic (SH) domain, our model learns to reconstruct high-resolution HRTFs from sparse input measurements with significantly improved accuracy. To enhance spatial coherence, we introduce a neighbor dissimilarity loss that promotes magnitude smoothness, yielding more realistic upsampling. We evaluate our method using both perceptual localization models and objective spectral distortion metrics. Experiments show that our model surpasses leading methods by a substantial margin in generating realistic, high-fidelity HRTFs.
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 HRTFformer, a novel transformer-based model for personalized HRTF upsampling that significantly improves spatial audio rendering accuracy. This work represents a meaningful advancement in the field of immersive audio, combining innovative methodologies with rigorous experimental validation to address a critical challenge in personalized audio experiences.
Methodology
The proposed methodology, HRTFformer, leverages a transformer-based architecture to address the challenges of HRTF upsampling. The use of spherical harmonics for data representation is innovative, allowing the model to effectively capture spatial correlations. The introduction of a neighbor dissimilarity loss function is a significant contribution that enhances spatial coherence, addressing a key limitation in existing methods. The combination of global attention mechanisms with local feature extraction through convolutional blocks is well-justified and effectively implemented.
Results & evaluation
The experiments are comprehensive, utilizing a well-defined dataset (SONICOM HRTF dataset) and evaluating the model across varying sparsity levels. The results demonstrate that HRTFformer outperforms existing methods in both objective metrics (LSD, ILD, ITD) and perceptual evaluations, indicating robust performance in realistic scenarios. The inclusion of ablation studies further strengthens the evaluation by isolating the contributions of different model components.
Reproducibility
The paper provides sufficient implementation details, including model architecture, training parameters, and evaluation metrics. However, the absence of a publicly available code repository or demo limits reproducibility. Future work could benefit from sharing the model and dataset to facilitate further research.
Limitations
While the model shows strong performance, the reliance on perceptual models for evaluations may not fully capture human listener experiences. Additionally, the paper acknowledges the limitations of dataset diversity, which could affect generalization to unseen subjects. Future work should address these limitations through subjective evaluations and potentially expanding the dataset.
Broader impact
The advancements in personalized HRTF rendering have significant implications for immersive audio applications, including virtual and augmented reality, gaming, and assistive technologies for the hearing impaired. By improving the practicality of personalized HRTF measurements, this work could enhance user experiences across various domains. The main contribution of this paper is the introduction of HRTFformer, a novel transformer-based model for personalized HRTF upsampling that significantly improves spatial audio rendering accuracy. This work represents a meaningful advancement in the field of immersive audio, combining innovative methodologies with rigorous experimental validation to address a critical challenge in personalized audio experiences.
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