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.

探究如何利用卷积神经网络 (CNNs) 从对数 - 梅尔幅度谱图中有效地学习音色表示，提出了一种设计策略，旨在捕捉用于学习音质的相关时频上下文，并且设计了多个基于该策略的 CNN 体系结构。这些体系结构成功地应用于与音色相关的不同研究任务。