  Machine hearing is an emerging area. Motivated by the need of a principled
framework across domain applications for machine listening, we propose a
generic and data-driven representation learning approach. For this sake, a
novel and efficient supervised dictionary learning method is presented.
Experiments are performed on both computational auditory scene (East Anglia and
Rouen) and synthetic music chord recognition datasets. Obtained results show
that our method is capable to reach state-of-the-art hand-crafted features for
both applications
