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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2110.03630 (eess)
[Submitted on 7 Oct 2021 (v1), last revised 11 May 2022 (this version, v2)]

Title:Towards Faster Continuous Multi-Channel HRTF Measurements Based on Learning System Models

Authors:Tobias Kabzinski, Peter Jax
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Abstract:Measuring personal head-related transfer functions (HRTFs) is essential in binaural audio. Personal HRTFs are not only required for binaural rendering and for loudspeaker-based binaural reproduction using crosstalk cancellation, but they also serve as a basis for data-driven HRTF individualization techniques and psychoacoustic experiments. Although many attempts have been made to expedite HRTF measurements, the rotational velocities in today's measurement systems remain lower than those in natural head movements. To cope with faster rotations, we present a novel continuous HRTF measurement method. This method estimates the HRTFs offline using a Kalman smoother and learns state-space parameters, including the system model, on short signal segments, utilizing the expectation maximization algorithm. We evaluated our method in simulated single-channel and multi-channel measurements using a rigid sphere HRTF model. Comparing with conventional methods, we found that the system distances are improved by up to 30 dB.
Comments: 5 pages, 4 figures, minor changes compared to v1 after reviewers' feedbacks, accepted at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subjects: Audio and Speech Processing (eess.AS); Sound (cs.SD)
Cite as: arXiv:2110.03630 [eess.AS]
  (or arXiv:2110.03630v2 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2110.03630
Related DOI: https://doi.org/10.1109/ICASSP43922.2022.9746559

Submission history

From: Tobias Kabzinski [view email]
[v1] Thu, 7 Oct 2021 17:18:41 UTC (166 KB)
[v2] Wed, 11 May 2022 16:24:34 UTC (166 KB)
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