Collect and clean room impulse responses in noisy and uncontrolled environments.

Concept

Measuring how a room sounds — its reverberation, reflections, and spatial characteristics — is fundamental to acoustics research, architectural design, and audio production. But real-world measurements are messy: background noise, non-stationary disturbances, and equipment limitations corrupt the signals we rely on.

Our research develops robust measurement techniques and analysis tools that extract reliable acoustic information even under difficult conditions, and makes the resulting data accessible for further processing and simulation.

Swept-Sine Measurement in Noise

Real acoustic measurements are often contaminated by non-stationary noise — footsteps, door slams, HVAC clicks. We developed the Rule of Two[5] and its short-term extension[7]: practical methods for identifying clean measurements among repeated sweeps in the presence of transient disturbances. Further work on noise removal[8] enables reliable data collection even in occupied, active spaces.

Code: mosaic_noise_removal — Python implementation of non-stationary noise removal from repeated swept-sine measurements.

Code: short-time-coherence-model — short-time coherence model for localizing noise events in sweep measurements[9].

Demo: Rule of Two listening examples — interactive demonstration of clean vs. corrupted sweep selection.

Calibrating Reverberation Models

The classic Sabine and Eyring reverberation time formulas underpin room acoustics design, yet their empirical accuracy is rarely scrutinized. We revisited these formulas using over 5,000 measurements in the variable-acoustics lab Arni[2], providing updated calibrations[4] that improve prediction accuracy for practical room design.

Data: Arni dataset — room acoustic parameter measurements across thousands of absorption configurations.

Energy Decay Analysis

Extracting reverberation parameters from room impulse responses traditionally relies on iterative curve fitting that is fragile and requires manual tuning. DecayFitNet replaces this with a lightweight neural network[3] that estimates multi-exponential energy decay parameters in a single forward pass — deterministic, fast, and validated on 20,000+ real acoustic measurements.

Common-Slope Late Reverberation Model

Real rooms rarely exhibit simple single-exponential decay. The common-slope model[6] captures multi-exponential and directional decay behavior by separating shared decay slopes from direction-dependent amplitudes. This parametric model is particularly effective for coupled rooms and complex geometries, and enables efficient real-time rendering.

Code: CommonSlopeAnalysis — MATLAB toolkit for common-slope analysis of late reverberation.

Code: blind-multi-room-model — blind estimation of multi-room acoustic models from measurements. Dataset.

Demo: Common-slope listening examples — interactive demonstrations of common-slope analysis results.

Measurement Signal Design

Designing optimal excitation signals improves measurement quality at the source. We developed two-stage filter designs[10] for measurement processing that extract cleaner impulse responses from noisy recordings.

Code: Two_stage_filter — implementation of two-stage filter design for measurement signal processing.

Demo: Two-stage filter listening examples — audio comparisons.

References