MAD

The audio inpainting concept, recently proposed by the coordinator and colleagues, is a conceptual breakthrough that unifies in a single framework all the audio processing problems where data is partially missing or highly degraded. Instances of such problems are click removal, CD scratches restoration, declipping, packet loss concealment, source reconstruction in the time-frequency domain and bandwidth extension. While these tasks had been addressed separately in the past, the audio inpainting unified formulation as an inverse problem is a promising abstraction to factorize the main difficulties shared among tasks, to provide methods that outperform state-of-the-art techniques on existing tasks and to address new problems where missing data reconstruction has been too difficult a task so far. The MAD proposal develops audio inpainting for any task involving missing audio data. The main objectives of this proposal are: a) to deploy the concept of audio inpainting within the research community by proposing new approaches, by addressing new applications and by creating and animating a dedicated research network; b) to initiate works on time-frequency inpainting, i.e. on the reconstruction of missing coefficients in a transform domain; c) to expand the concept of and the techniques for audio inpainting by developing connections with machine learning. The project establishes strong relations between signal processing and machine learning. It does not only consist in applying machine learning techniques to signals but also deals with a machine learning formulation of signal processing problems and with the integration of computational trade-offs in algorithms. The project also draws connections between audio and image processing. The proposal implies close interactions between theory and applications with top/down and bottom/up relations. All those original aspects are revealed in the composition of the team and are expected to result in powerful approaches to real applications. The MAD proposal is submitted to the ANR JCJC program under the leadership of Valentin Emiya, this proposal being the largest project he coordinates. To address its ambitious and diverse objectives, MAD involves a large team of 11 members, with research experiences in theory and application views from both academy and industry, signal processing and machine learning. Seven team members are located in the same site, the remaining four members being in three isolated distant sites including two members at Technicolor.