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Transformer 模型从稀疏输入重建音频空间化数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 HRIR-Former，这是一种新颖的时域 Transformer 模型，旨在从稀疏测量中重建任意方向的头部相关脉冲响应 (HRIR)。与通常在频域操作或依赖最小相位假设的先前方法不同，HRIR-Former 直接在时域中重建 HRIR，避免了潜在的时间保真度和空间连续性下降。该模型结合了正弦空间特征、Conv1D 精炼模块以及用于双耳时间差和水平差的辅助头，在 SONICOM 数据集上的性能优于现有技术。 AI

排序理由 该集群包含一篇详细介绍音频处理新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Transformer 模型从稀疏输入重建音频空间化数据

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该集群包含一篇详细介绍音频处理新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [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：基于空间编码Transformer的无网格时域头部相关脉冲响应重建

    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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