As we look to advance the state of the art in content-based music informatics, there is a general sense that progress is decelerating throughout the field. On closer inspection, performance trajectories across several applications reveal that this is indeed the case: hand-crafted feature design is sub-optimal and unsustainable, the power of shallow architectures is fundamentally limited, and short-time analysis cannot encode musically meaningful structure. Acknowledging breakthroughs in other perceptual AI domains, we offer that deep learning holds the potential to overcome each of these obstacles. Consequentially, we believe that deep learning can advance the state-of-the-art in music genre recognition.

We aim to quantitatively understand transport phenomena in multiphase flows, occurring in bulk, in confined geometries and in porous media. This is of importance for chemical reactor design and for enhanced oil/gas recovery. As such, this proposal is relevant for the top sectors ?Chemicals? and ?Energy?. For some of the subprojects, we have direct collaboration with the private sector (Shell, TetraPak).