Deep convolutional networks on the pitch spiral for musical instrument recognition

Understand

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.