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arXiv · 2604.26669

Full band denoising of room impulse response in the wavelet domain with dictionary learning

Théophile Dupré·Romain Couderc·Miguel Moleron·Axel Coulon·Rémy Bruno·Arnaud Laborie
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Abstract

Conventional wavelet-domain methods for room impulse response denoising rely on thresholding detail coefficients, which is unsuited for low frequencies. In this work, we introduce a wavelet-based post-processing algorithm that extends denoising to approximation coefficients by means of sparse dictionary learning with a time-varying error tolerance. The proposed method leverages an exponential decay envelope model to adapt reconstruction accuracy according to the local signal-to-noise ratio. This approach significantly improves low-frequency denoising of synthetic and measured room impulse responses compared to the baseline method, leading to more accurate estimation of acoustic parameters such as decay time.

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Théophile Dupré, Romain Couderc, Miguel Moleron, Axel Coulon, Rémy Bruno, Arnaud Laborie. 2026-04-29. Full band denoising of room impulse response in the wavelet domain with dictionary learning. https://arxiv.org/abs/2604.26669

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