用字典学习拓展小波域去噪，提升低频响应精度
Full band denoising of room impulse response in the wavelet domain with dictionary learning
- 通过时变误差容忍的稀疏字典学习，扩展去噪至近似系数
- 在合成与实测混响响应上，低频去噪性能显著优于基线方法
- 适合需要精确声学参数估计的音频处理场景
传统小波域混响脉冲响应去噪依赖细节系数阈值化，不适用于低频。本文提出一种基于小波域的后处理算法，通过时变误差容忍的稀疏字典学习，将去噪范围延伸至近似系数。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。相比基线方法，该方案在合成与实测混响响应中显著提升了低频去噪效果，从而更准确地估计声学参数，如衰减时间。
原文摘要 · Abstract (English)
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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