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Convolutional Neural Networks (CNNs) have been successfully used in variousMusic Information Retrieval (MIR) tasks, both as end-to-end models and asfeature extractors for more complex systems. However, the MIR field is stilldominated by the classical VGG-based CNN architecture variants, often incombination with more complex modules such as attention, and/or techniques suchas pre-training on large datasets. Deeper models such as ResNet -- whichsurpassed VGG by a large margin in other domains -- are rarely used in MIR. Oneof the main reasons for this, as we will show, is the lack of generalization ofdeeper CNNs in the music domain. In this paper, we present a principled way tomake deep architectures like ResNet competitive for music-related tasks, basedon well-designed regularization strategies. In particular, we analyze therecently introduced Receptive-Field Regularization and Shake-Shake, and showthat they significantly improve the generalization of deep CNNs onmusic-related tasks, and that the resulting deep CNNs can outperform currentmore complex models such as CNNs augmented with pre-training and attention. Wedemonstrate this on two different MIR tasks and two corresponding datasets,thus offering our deep regularized CNNs as a new baseline for these datasets,which can also be used as a feature-extracting module in future, more complexapproaches.

Receptive-Field Regularized CNNs for Music Classification and Tagging