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.
Figures & tables
Figure 1: Anechoic handclaps of eight different hand posture types. (Dashed line) Mean spectra over entire set of handclaps of that type. (Blue) one example spectrum and corresponding time-domain signal.
Figure 2: Handclap recording setup in the Aalto anechoic chamber.
Method
LEDCdB↓
ΔEDT↓
LLSE↓
LNRMSE↓
Measured
Known exc. (unreg.)
−30.8 dB
8.7 ms
0.071
0.061
Known exc. (Tikhonov)
−41.2 dB
1.8 ms
0.031
0.022
Windowed exc. (3 ms)
−18.3 dB
93 ms
0.20
2.7
Windowed exc. (6 ms)
−17.4 dB
126 ms
0.27
3.0
Neural regressor
−22.2 dB
56.9 ms
0.112
0.733
Simulated
Known exc. (unreg.)
−52.0 dB
0.6 ms
0.0035
0.060
Table 1: Mean rir estimation results for both measured and simulated rir . Known-excitation rows are references; best unknown-excitation result per metric and block is shown in bold.
Figure 3: Phone-recorded handclaps (left) and corresponding rir estimates obtained with the neural regressor (right) for eight handclap positions in two rooms, showing spectra and RMS envelopes
We propose a self-supervised approach for learning room impulse response (RIR) representations from single-channel noisy-reverberant speech. It consists of first training on reverberant data, then on noisy-reverberant data, and finally with a teacher-student approach, where the student learns to replicate the teacher's embeddings when given a noisy version of the reverberant input. We assess their representational capabilities by estimating acoustic room parameters from them. Conditioning a discriminative speech enhancement model on the derived embeddings yields consistent gains across all evaluated metrics, including downstream word error rate, for both reverberant and noisy-reverberant speech.
Single-channel speaker distance estimation has recently achieved centimeter-level accuracy in simulated environments, yet it remains unclear which components of the room impulse response (RIR) the model exploits and how performance depends on the recording conditions. In this work, we decompose simulated RIRs into four variants (full, direct-only, no-late, and no-early) using the mixing time estimated from the echo density function as the boundary between early reflections and late reverberation. We define four calibration scenarios, from fully calibrated (synchronised capture, known source level) to fully uncalibrated (arbitrary onset, unknown level), and evaluate all combinations on a matched dataset. Results show that without time calibration, mean absolute error (MAE) increases to 1.29 m and the model extracts reverberation-based cues, with early reflections emerging as the most informative component. Further analysis against DRR, C50, and T60 confirms that estimation accuracy improves with stronger early energy and degrades in highly reverberant environments. When time calibration is available, the model achieves a MAE of 0.14 m by extracting the propagation delay alone, regardless of the RIR content.
Michael Neri, Archontis Politis, Tuomas Virtanen
Faculty of Information Technology and Communication Sciences, Tampere University, Finland
Image-source-method (ISM)-based room impulse response (RIR) simulation is a useful and physically interpretable tool for acoustic scene modeling, but full-order ISM becomes computationally expensive as the reflection order and room complexity increase. We propose a physics-guided framework for fast RIR simulation that preserves the geometric structure of ISM while learning to retain only acoustically important image-source paths during online traversal. To recover energy removed by pruning, the proposed PathRIR uses a lightweight compensation multilayer perceptron to predict the missing late-tail energy envelope and generate a compensation tail whose energy follows that envelope. Experiments on irregular 3D rooms show that PathRIR reduces image-source computation and improves runtime efficiency over a full-order ISM simulator, while achieving low waveform- and decay-related errors. Ablation results show that adding the compensation tail improves waveform fidelity and reduces energy-decay-curve error, reverberation-time error, and direct-to-reverberant-ratio error, with modest runtime overhead.
Shaoheng Xu, Chunyi Sun, Jihui Zhang +3
The Australian National University, Australia · The University of Queensland, Australia