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.

翻译：传统的小波域房间脉冲响应去噪方法依赖于对细节系数的阈值处理，但这不适用于低频段。本文提出一种基于小波的后处理算法，通过时变误差容忍度的稀疏字典学习，将去噪扩展至逼近系数。该方法利用指数衰减包络模型，根据局部信噪比自适应调整重建精度。与基线方法相比，本方法显著改善了合成和实测房间脉冲响应的低频去噪效果，从而更精确地估计诸如衰变时间等声学参数。