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

TL;DR

This paper presents a novel wavelet-based denoising method for room impulse responses that effectively enhances low-frequency noise reduction using dictionary learning and adaptive error tolerance.

Contribution

It introduces a new post-processing algorithm that extends denoising to approximation coefficients with sparse dictionary learning and adaptive error control.

Findings

Significantly improves low-frequency denoising of room impulse responses.

Leads to more accurate estimation of acoustic parameters like decay time.

Outperforms baseline methods on synthetic and measured data.

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.