从拍手声中估计房间冲激响应

中文摘要 AI 辅助

拍手声为房间声学提供了一种无需设备的激励，但其未知且可变的源波形使得房间冲激响应（RIR）的估计具有挑战性。在这项工作中，我们研究了是否可以直接从拍手声中估计RIR。为此，我们引入了一个消声拍手数据集，包含来自17名参与者的2,540次拍手，旨在捕捉自然拍手和不同手部配置之间的变异性。我们首先建立了当激励拍手已知时，使用正则化反卷积可达到的性能，并表明通过从混响录音中对直达声进行加窗来近似未知激励是不充分的。为了在没有已知激励的情况下估计RIR，我们提出使用消声拍手录音来训练一个具有监督回归目标的深度神经网络。在受控的合成基准上评估，所提出的神经回归器在所有仪器指标上显著优于基于加窗的基线方法。此外，我们在真实声学空间中测量的拍手录音上测试了所提出的方法，表明在同一房间位置的不同拍手测量中推断出的RIR频谱是一致的。这些结果展示了在不需要知道激励信号的情况下，直接从自然拍手声中估计RIR的可行性。

英文摘要

Handclaps provide an equipment-free excitation for room acoustics, but their unknown and variable source waveform makes room impulse response (RIR) estimation challenging. In this work, we investigate whether RIRs can be estimated directly from handclaps. To this end, we introduce an anechoic handclap dataset containing 2,540 claps from 17 participants, designed to capture variability across natural claps and different hand configurations. We first establish the performance attainable when the excitation clap is known using regularized deconvolution, and show that approximating the unknown excitation by windowing the direct sound from the reverberant recording is insufficient. To estimate the RIR without a known excitation, we propose using the anechoic handclap recordings to train a deep neural network with a supervised regression objective. Evaluated on a controlled synthetic benchmark, the proposed neural regressor significantly outperforms windowing-based baselines across all instrumental metrics. Furthermore, we test the proposed method on handclap recordings measured in real acoustic spaces, showing that the inferred RIR spectra are consistent across different handclap measurements taken in the same room location. These results showcase the feasibility of directly estimating RIRs from natural handclaps without requiring knowledge of the excitation signal.

发表机构

• National Taiwan University（国立台湾大学）
• Acoustics Lab, DICE, Aalto University（阿尔托大学DICE声学实验室）
• MoonShine Animation Studio（月光动画工作室）

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