Speaker separation for hearing aids with small-footprint deep learning methods (C06*)

• Subject Area: Acoustics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 352015383

This project explores deep learning for acoustic separation of speakers' signals captured with hearing aids. The solutions to be developed will be compatible with small-footprint hardware and should contribute to improving the communication ability of the respective user. This will be achieved by combining state-of-the-art speaker separation strategies based on recurrent architectures with auditory models of perception for hearing-aid processing in realistic environments. The project will advance training algorithms suitable for complex binaural scenes, the preservation of binaural cues in the context of speech separation, as well as quality measures of separated signals.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1330: Hearing acoustics: Perceptual principles, Algorithms and Applications (HAPPAA)
• Applicant Institution: Carl von Ossietzky Universität Oldenburg
• Project Head: Professor Dr. Bernd T. Meyer