Estimation of Room Impulse Responses from Handclaps

2026-09-24

AI Summary

This study addresses the challenge of accurately estimating room impulse responses (RIRs) from natural handclaps, whose excitation waveforms are inherently unknown and highly variable. To overcome this limitation, we propose a deep learning-based supervised regression method that estimates RIRs end-to-end directly from reverberant clap recordings. By training a deep neural network on a constructed anechoic handclap dataset, our approach eliminates the reliance on known excitation signals required by conventional acoustic measurements, thereby enabling device-free acoustic sensing. Experimental evaluations demonstrate that the proposed method significantly outperforms existing baselines on synthetic benchmarks. Furthermore, validation in real-world environments confirms the consistency of the estimated RIRs, substantiating both the feasibility and practical value of the approach for real-world acoustic analysis.

Abstract

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.

Problem

• Room Impulse Response
• Handclaps
• Acoustic Measurement
• Excitation Estimation

Innovation

• Room Impulse Response
• Handclap Dataset
• Deep Neural Network
• Supervised Regression
• Anechoic Recordings