  Machine hearing or listening represents an emerging area. Conventional
approaches rely on the design of handcrafted features specialized to a specific
audio task and that can hardly generalized to other audio fields. For example,
Mel-Frequency Cepstral Coefficients (MFCCs) and its variants were successfully
applied to computational auditory scene recognition while Chroma vectors are
good at music chord recognition. Unfortunately, these predefined features may
be of variable discrimination power while extended to other tasks or even
within the same task due to different nature of clips. Motivated by this 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.
The method learns dissimilar dictionaries, one per each class, in order to
extract heterogeneous information for classification. In other words, we are
seeking to minimize the intra-class homogeneity and maximize class
separability. This is made possible by promoting pairwise orthogonality between
class specific dictionaries and controlling the sparsity structure of the audio
clip's decomposition over these dictionaries. The resulting optimization
problem is non-convex and solved using a proximal gradient descent method.
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.
