构建7阶全向声学脉冲响应数据集，支持高精度沉浸式音频还原。
HARP: A Large-Scale Higher-Order Ambisonic Room Impulse Response Dataset
- 基于图像源法生成7阶全向声学脉冲响应，直接在球谐函数域采集
- 采用64麦克风阵列配置，覆盖更广声场，提升空间分辨率与保真度
- 适用于声源定位、混响预测等任务，尤其适合机器学习建模研究
本文介绍一个7阶全向声学脉冲响应（HOA-RIRs）的大规模数据集，通过图像源法生成。利用更高阶的全向声学技术，该数据集支持精确的空间音频再现，满足真实沉浸式音频应用的需求。借助虚拟仿真，提出一种基于叠加原理的独特麦克风配置，优化声场覆盖并克服传统麦克风阵列的局限性。所提出的64麦克风配置可直接在球谐函数域捕捉脉冲响应。数据集涵盖多种房间几何结构、吸声材料及声源-接收器距离变化。同时提供详细的仿真设置说明，确保结果可复现。该数据集是空间音频研究的重要资源，特别适用于机器学习改进房间声学建模与声场合成。其高空间分辨率和逼真度对声源定位、混响预测和沉浸式音频还原至关重要。
原文摘要 · Abstract (English)
This contribution introduces a dataset of 7th-order Ambisonic Room Impulse Responses (HOA-RIRs), created using the Image Source Method. By employing higher-order Ambisonics, our dataset enables precise spatial audio reproduction, a critical requirement for realistic immersive audio applications. Leveraging the virtual simulation, we present a unique microphone configuration, based on the superposition principle, designed to optimize sound field coverage while addressing the limitations of traditional microphone arrays. The presented 64-microphone configuration allows us to capture RIRs directly in the Spherical Harmonics domain. The dataset features a wide range of room configurations, encompassing variations in room geometry, acoustic absorption materials, and source-receiver distances. A detailed description of the simulation setup is provided alongside for an accurate reproduction. The dataset serves as a vital resource for researchers working on spatial audio, particularly in applications involving machine learning to improve room acoustics modeling and sound field synthesis. It further provides a very high level of spatial resolution and realism crucial for tasks such as source localization, reverberation prediction, and immersive sound reproduction.
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