The Winners of the SONICOM LAP Challenge

The winners of the SONICOM Listener Acoustic Personalisation (LAP) Challenge were announced at the 32nd European Signal Processing Conference (EUSIPCO 24) on 29 August in Lyon, France.

Sponsored by the IEEE Signal Processing Society, the LAP Challenge invited members of the auditory research community to tackle key challenges facing immersive audio technology, advance the state of the art, and contribute to the development of standardised metrics for personalised spatial audio.

The inaugural edition of the challenge concentrated on two fundamental aspects of head-related transfer functions (HRTFs): spatial sampling and interpolation. Teams were challenged to submit their solutions that address one of two tasks:

Task 1: HRTF normalisation for merging different HRTF datasets

In this task, teams were given a set of HRTFs measured from different individuals in different labs (i.e. different measurement setups, different equipment, different space, and distances, etc.) and challenged to harmonise the sets to compensate for the influences of the measurement setup.

The first prize for Task 1 was awarded to Jiale Zhao, Dingding Yao, Zelin Qiu, Chengzhong Wang, and Junfeng Li for their solution: “Normalization of Head-Related Transfer Functions Based on Neural Networks”.

LAP Challenge Winner Task 1

Task 2: spatial upsampling for obtaining a high-spatial-resolution HRTF from a very low number of directions

In this task, teams were given a sparse set of HRTF measurements and challenged to provide a reconstructed HRTF set in high spatial resolution.

The first prize for Task 2 was awarded to Yoshiki Masuyama, Gordon Wichern, Francois G. Germain, Christopher Ick, and Jonathan Le Roux for their solution: “Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization”.

Congratulations to all the winners! A draft technical report covering the full results of the challenge is available here (a complete report and DOI will be available soon): LAP Challenge Technical Report


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