  Traditional methods to tackle many music information retrieval tasks
typically follow a two-step architecture: feature engineering followed by a
simple learning algorithm. In these "shallow" architectures, feature
engineering and learning are typically disjoint and unrelated. Additionally,
feature engineering is difficult, and typically depends on extensive domain
expertise.
  In this paper, we present an application of convolutional neural networks for
the task of automatic musical instrument identification. In this model, feature
extraction and learning algorithms are trained together in an end-to-end
fashion. We show that a convolutional neural network trained on raw audio can
achieve performance surpassing traditional methods that rely on hand-crafted
features.
