Listener Acoustic Personalisation (LAP) Challenge 2024

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Listener Acoustic Personalisation (LAP) Challenge 2024


Supported by the SPS Challenge Program.

Personalised Head-Related Transfer Functions (HRTFs) have been shown to enhance auditory localization and immersion in mixed realities. However, relevant issues, such as the accurate acquisition of user-specific anatomical data, efficient simulation algorithms, and effective user validation, do not converge into a common and internationally recognized benchmark for evaluating HRTFs.

The LAP Challenge endeavours to provide a platform where researchers can explore these challenges, advance state-of-the-art, and contribute to developing standardized metrics for personalised spatial audio. The 2024 inaugural edition will concentrate on two fundamental aspects of HRTF: spatial sampling and interpolation. Teams are challenged to submit their solutions that address one of two tasks.

Task 1. HRTF spatial re-sampling for merging different HRTF datasets

Task 2. HRTF spatial up-sampling for obtaining a high-spatial-resolution HRTF from a very low number of directions

The LAP Challenge is also meant to share and exploit the open outputs of the EU-H2020 FET SONICOM project, which includes the SONICOM HRTF dataset providing HRTF data, headphone transfer functions, 3D scans of ears, head, and torso, and depth images for more than 200 subjects. Additionally, other available resources include the Spatially Oriented Format for Acoustics (AES69SOFA standard), the Auditory Modeling Toolbox (AMT), and the numerical computation tool Mesh2HRTF, to name but a few, also actively supported by the SONICOM project.

Aspiring to become a research community catalyst for HRTF processing, the LAP Challenge will encourage further endeavours in the management and standardisation of spatially oriented acoustics and auditory data through an engaging and competitive initiative involving several signal processing communities.

  • Primary: Audio and Acoustic Signal Processing
  • Secondary: Multimedia Signal Processing, Machine Learning for Signal Processing

Visit the Challenge website for details and more information!


Technical Committee: Audio and Acoustic Signal Processing


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