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Musical performance combines a wide range of pitches, nuances, and expressivetechniques. Audio-based classification of musical instruments thus requires tobuild signal representations that are invariant to such transformations. Thisarticle investigates the construction of learned convolutional architecturesfor instrument recognition, given a limited amount of annotated training data.In this context, we benchmark three different weight sharing strategies fordeep convolutional networks in the time-frequency domain: temporal kernels;time-frequency kernels; and a linear combination of time-frequency kernelswhich are one octave apart, akin to a Shepard pitch spiral. We provide anacoustical interpretation of these strategies within the source-filterframework of quasi-harmonic sounds with a fixed spectral envelope, which arearchetypal of musical notes. The best classification accuracy is obtained byhybridizing all three convolutional layers into a single deep learningarchitecture.

Deep convolutional networks on the pitch spiral for musical instrument recognition