Tackling hard problems in audio with Data-Efficient Non-linear InverSe mEthods – DENISE

DENISE aims at fundamental methodological contributions to the field of audio signal processing. Its promises are data-requirement savings and performance leaps that, in the long run, underlie strong economical and ecological benefits for the quickly-growing application field of audio technologies.

The state of affairs is a ubiquitous and successful use of deep learning methods across all areas of audio signal processing. This is justified by their remarkable ability to approximate arbitrary non-linear functions given sufficiently large training datasets to learn from.

DENISE's central premise, however, is that a number of key sub-problems in audio may be tackled without any learning thanks to recent theoretical and methodological breakthroughs in the field of non-linear inverse problems, which have come largely unnoticed by the audio research community as of yet. Fundamental research efforts will be carried out to unlock the full potential of these findings in two emerging and challenging applications: audio inpainting, i.e., the recovery of completely missing samples, and echo-aware multichannel processing.

Far from giving up the power of machine learning, project DENISE advocates the development of hybrid approaches that fully leverage the potential of both analytical and learned solutions, with data-efficiency at its core.