U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model

--This paper explores the outcome of training state-of-the-art dereverberation models with supervision settings ranging from weakly-supervised to fully unsupervised, relying solely on reverberant signals and an acoustic model for training. Most of the existing deep learning approaches typically require paired dry and reverberant data, which are difficult to obtain in practice. We develop instead a sequential learning strategy motivated by a bayesian formulation of the dereverberation problem, wherein acoustic parameters and dry signals are estimated from reverberant inputs using deep neural networks, guided by a reverberation matching loss. COUSTIC waves propagation in enclosed environments is significantly influenced by reflections and diffractions from surrounding surfaces and objects. These interactions alter the original waveform and result in reverberation, which can be modeled as a superposition of delayed and attenuated versions of the source signal. Reverberation has long been recognized as a critical factor affecting speech intelligibility [1], and its detrimental effects on audio clarity have motivated decades of research. The task of reverberation suppression, commonly referred to as dereverberation, has received renewed attention in recent years due to its relevance in a wide range of audio processing applications. Effective dereverberation is essential in enhancing the performance of hearing aids [2], improving communication quality in hands-free [3] telephony, and enabling robust Automatic Speech Recognition (ASR) in human-machine interaction scenarios [4]. It also serves as a key preprocessing step in general-purpose speech enhancement frameworks [5]. Beyond suppression, reverberation itself plays a constructive role in audio production, particularly in simulating desired acoustic characteristics in post-processing. Reverberation conversion, or acoustic transfer, aims to transform a given recording, possibly containing unknown or undesired room effects, into a version consistent with a target acoustic environment. This work was funded by the European Union (ERC, HI-Audio, 101052978). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council.