  The work of a single musician, group or composer can vary widely in terms of
musical style. Indeed, different stylistic elements, from performance medium
and rhythm to harmony and texture, are typically exploited and developed across
an artist's lifetime. Yet, there is often a discernable character to the work
of, for instance, individual composers at the perceptual level - an experienced
listener can often pick up on subtle clues in the music to identify the
composer or performer. Here we suggest that a convolutional network may learn
these subtle clues or features given an appropriate representation of the
music. In this paper, we apply a deep convolutional neural network to a large
audio dataset and empirically evaluate its performance on audio classification
tasks. Our trained network demonstrates accurate performance on such
classification tasks when presented with 5 s examples of music obtained by
simple transformations of the raw audio waveform. A particularly interesting
example is the spectral representation of music obtained by application of a
logarithmically spaced filter bank, mirroring the early stages of auditory
signal transduction in mammals. The most successful representation of music to
facilitate discrimination was obtained via a random matrix transform (RMT).
Networks based on logarithmic filter banks and RMT were able to correctly guess
the one composer out of 31 possibilities in 68 and 84 percent of cases
respectively.
