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This study introduces a new method for creating personalized head-related transfer functions (HRTFs) using sparse measurements and a convolutional neural network (CNN). This approach enhances virtual reality audio experiences by accurately reconstructing HRTFs.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Computer Science

Background:

  • Individualized head-related transfer functions (HRTFs) are crucial for realistic spatial audio.
  • Traditional HRTF measurement is time-consuming and requires high spatial resolution.
  • Anthropometric modeling offers an alternative but may lack accuracy.

Purpose of the Study:

  • To develop an HRTF individualization method using spatially sparse measurements.
  • To leverage convolutional neural networks (CNNs) for HRTF reconstruction.
  • To improve the efficiency and accuracy of creating personalized HRTFs.

Main Methods:

  • HRTFs were represented as 2D images (direction vs. frequency).
  • A CNN was trained using sparse HRTF measurements as input and high-resolution HRTFs as output.
  • The trained CNN reconstructed individual HRTFs from new, sparsely measured data.

Main Results:

  • Objective evaluation showed spectral distortion (SD) of ~4.4 dB with 23 sparse directions and ~3.8 dB with 105 directions.
  • Subjective tests indicated that HRTFs reconstructed from 105 directions were perceptually superior to the baseline.
  • The method demonstrated effective HRTF recovery with significantly reduced measurement points.

Conclusions:

  • The proposed CNN-based method efficiently individualizes HRTFs using sparse measurements.
  • This approach combines spectral and spatial HRTF characteristics for accurate reconstruction.
  • The method holds significant potential for enhancing virtual reality (VR) audio immersion.