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Transformer Model Reconstructs Audio Spatialization Data From Sparse Inputs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HRIR-Former, a novel time-domain Transformer model designed to reconstruct Head-Related Impulse Responses (HRIRs) at arbitrary directions from sparse measurements. Unlike previous methods that often operate in the frequency domain or rely on minimum-phase assumptions, HRIR-Former reconstructs HRIRs directly in the time domain, avoiding potential degradation in temporal fidelity and spatial continuity. The model incorporates sinusoidal spatial features, a Conv1D refinement module, and auxiliary heads for interaural time and level differences, demonstrating improved performance on the SONICOM dataset compared to existing techniques. AI

RANK_REASON The cluster contains a research paper detailing a new model for audio processing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Transformer Model Reconstructs Audio Spatialization Data From Sparse Inputs

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The cluster contains a research paper detailing a new model for audio processing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shaoheng Xu, Chunyi Sun, Jihui Zhang, Amy Bastine, Prasanga N. Samarasinghe, Thushara D. Abhayapala, Hongdong Li ·

    HRIR-Former: Grid-Free Time-Domain Reconstruction of Head-Related Impulse Responses with a Spatially Encoded Transformer

    arXiv:2603.27998v2 Announce Type: replace-cross Abstract: Individualized head-related impulse responses (HRIRs) enable binaural rendering, but dense per-listener measurements are costly. We address HRIR spatial up-sampling from sparse per-listener measurements: given a few measur…

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