  Musical performance combines a wide range of pitches, nuances, and expressive
techniques. Audio-based classification of musical instruments thus requires to
build signal representations that are invariant to such transformations. This
article investigates the construction of learned convolutional architectures
for instrument recognition, given a limited amount of annotated training data.
In this context, we benchmark three different weight sharing strategies for
deep convolutional networks in the time-frequency domain: temporal kernels;
time-frequency kernels; and a linear combination of time-frequency kernels
which are one octave apart, akin to a Shepard pitch spiral. We provide an
acoustical interpretation of these strategies within the source-filter
framework of quasi-harmonic sounds with a fixed spectral envelope, which are
archetypal of musical notes. The best classification accuracy is obtained by
hybridizing all three convolutional layers into a single deep learning
architecture.
