dereverberation

Dereverberation is the process of reducing reverberation in audio signals to recover a clearer, more intelligible version of the original sound. Reverberation arises from sound reflections off surfaces in enclosed spaces, creating a tail of overlapping copies that can smear temporal structure and blur spectral details. The goal is to suppress late reflections while preserving early reflections that contribute to spatial impression.

Methods vary by setup. Single-channel approaches include spectral subtraction, inverse filtering, and adaptive filtering, but these can distort the signal if the reverberation model is inaccurate. Multichannel techniques exploit spatial information; the Weighted Prediction Error (WPE) method is widely used to model late reverberation as a predictable component from past frames and subtract it. Other multichannel options include beamforming-based dereverberation and multichannel Wiener filtering. Data-driven approaches employ neural networks to map reverberant signals to dereverberated ones, operating in the time domain or the time-frequency domain and sometimes jointly estimating room impulse responses.

Applications include improving speech intelligibility in telecommunication, videoconferencing, hearing aids, and automatic speech recognition, as well as reducing room reverberation in music production and broadcasting.

Evaluation typically uses objective measures such as PESQ, POLQA, STOI, and SRMR, alongside perceptual listening tests. Performance depends on room acoustics, reverberation time, and microphone configuration, with challenges including preserving natural timbre and avoiding artifacts, handling nonstationary reverberation, and managing computational requirements.

Dereverberation is related to, but distinct from, denoising and echo cancellation and often involves estimation of the room impulse response or blind deconvolution techniques.