  The focus of this work is to study how to efficiently tailor Convolutional
Neural Networks (CNNs) towards learning timbre representations from log-mel
magnitude spectrograms. We first review the trends when designing CNN
architectures. Through this literature overview we discuss which are the
crucial points to consider for efficiently learning timbre representations
using CNNs. From this discussion we propose a design strategy meant to capture
the relevant time-frequency contexts for learning timbre, which permits using
domain knowledge for designing architectures. In addition, one of our main
goals is to design efficient CNN architectures -- what reduces the risk of
these models to over-fit, since CNNs' number of parameters is minimized.
Several architectures based on the design principles we propose are
successfully assessed for different research tasks related to timbre: singing
voice phoneme classification, musical instrument recognition and music
auto-tagging.
