PhD defense Louis Bahrman: Acoustics-aware hybrid deep neural dereverberation
Télécom Paris, 19 place Marguerite Perey F-91120 Palaiseau [getting there], amphi 6 and in videoconferencing
Jury
- Emanuël HABETS, International Audio Laboratories, Erlangen, Reviewer
- Axel ROEBEL, IRCAM, Reviewer
- Nelly PUSTELNIK, ENS de Lyon, Examiner
- Augusto SARTI, Politecnico di Milano, Examiner
- Gaël RICHARD, Télécom Paris, Thesis Supervisor
- Mathieu FONTAINE, Télécom Paris, Co-Supervisor
Abstract
The aim of this thesis is to leverage room acoustics models in deep-learning-based approaches for dereverberation. Audio signals are often altered by reverberation effects induced by objects and walls of the room in which they propagate, leading to a loss in intelligibility. However, most deep learning methods developed to tackle this problem can be considered as black-box systems, as they are purely data-driven and not interpretable from a physical perspective.
After studying whether neural dereverberators are consistent with physical reverberation models, we propose two hybrid approaches to train a dereverberation model in a physically realistic manner. The first one regularizes the training loss to encourage a deep neural network to produce realistic solutions, and the second is motivated by a maximum-likelihood formulation of the problem and consists in an unsupervised learning strategy that integrates a reverberation model into a deep learning framework.