HRTF Interpolation using a Spherical Neural Process Meta-Learner

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Several individualization methods have recently been proposed to estimate a subject's Head-Related Transfer Function (HRTF) using convenient input modalities such as anthropometric measurements or pinnae photographs. There exists a need for adaptively correcting the estimation error committed by such methods using a few data point samples from the subject's HRTF, acquired using acoustic measurements or perceptual feedback. To facilitate this, we introduce a Convolutional Conditional Neural Process meta-learner specialized in HRTF error interpolation. In particular, the model includes a Spherical Convolutional Neural Network component to accommodate the spherical geometry of HRTF data. It also exploits potential symmetries between the HRTF's left and right channels about the median plane. In this work, we evaluate the proposed model's performance purely on time-aligned spectrum interpolation grounds under a simplified setup where a generic population-mean HRTF forms the initial estimates prior to corrections instead of individualized ones. The trained model achieves up to 3 dB relative error reduction compared to state-of-the-art interpolation methods despite being trained using only 85 subjects. This improvement translates up to nearly a halving of the data point count required to achieve comparable accuracy, in particular from 50 to 28 points to reach an average of -20 dB relative error per interpolated feature. Moreover, we show that the trained model provides well-calibrated uncertainty estimates. Accordingly, such estimates could inform the sequential decision problem of acquiring as few correcting HRTF data points as needed to meet a desired level of HRTF individualization accuracy.

Original languageEnglish
Pages (from-to)1790-1802
Number of pages13
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Early online date2024
Publication statusPublished - 2024
MoE publication typeA1 Journal article-refereed


  • Acoustic measurements
  • Adaptation models
  • Audio systems
  • Data models
  • Interpolation
  • Predictive models
  • Representation learning
  • Solid modeling
  • Spatial audio
  • Uncertainty
  • representation learning
  • spatial audio
  • uncertainty


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