用生成数据增强合成房间声学，提升说话人距离估计性能
Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation
- 通过生成模型扩充房间冲激响应数据集
- 解决真实测量或仿真多房间声学的高成本难题
- 适合做声学建模与空间感知任务的研究者
本文介绍了作为ICASSP 2025生成数据增强研讨会一部分的房间声学合成挑战。该挑战定义了一个独特的生成任务，旨在提升房间冲激响应数据集的数量与多样性，以支持对空间敏感的下游任务——说话人距离估计。由于精确测量或模拟多个房间的声学特性存在技术困难，挑战提出采用生成数据增强作为替代方案，有望提升各类下游任务的表现。挑战官网、数据集及评估代码已公开：https://sites.google.com/view/genda2025。
原文摘要 · Abstract (English)
This paper describes the synthesis of the room acoustics challenge as a part of the generative data augmentation workshop at ICASSP 2025. The challenge defines a unique generative task that is designed to improve the quantity and diversity of the room impulse responses dataset so that it can be used for spatially sensitive downstream tasks: speaker distance estimation. The challenge identifies the technical difficulty in measuring or simulating many rooms' acoustic characteristics precisely. As a solution, it proposes generative data augmentation as an alternative that can potentially be used to improve various downstream tasks. The challenge website, dataset, and evaluation code are available at https://sites.google.com/view/genda2025.
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