Full band denoising of room impulse response in the wavelet domain with dictionary learning
For acoustic signal processing researchers, this method improves low-frequency denoising of room impulse responses, leading to better acoustic parameter estimation.
The paper introduces a wavelet-based denoising method for room impulse responses that extends denoising to low frequencies via sparse dictionary learning with time-varying error tolerance, achieving improved low-frequency denoising and more accurate decay time estimation compared to baseline.
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.