In the age of music streaming platforms, the task of automatically tagging music audio has garnered significant attention, driving researchers to devise methods aimed at enhancing performance metrics on standard datasets. Most recent approaches rely on deep neural networks, which, despite their impressive performance, possess opacity, making it challenging to elucidate their output for a given input. While the issue of interpretability has been emphasized in other fields like medicine, it has not received attention in music-related tasks. In this study, we explored the relevance of interpretability in the context of automatic music tagging. We constructed a workflow that incorporates three different information extraction techniques: a) leveraging symbolic knowledge, b) utilizing auxiliary deep neural networks, and c) employing signal processing to extract perceptual features from audio files. These features were subsequently used to train an interpretable machine-learning model for tag prediction. We conducted experiments on two datasets, namely the MTG-Jamendo dataset and the GTZAN dataset. Our method surpassed the performance of baseline models in both tasks and, in certain instances, demonstrated competitiveness with the current state-of-the-art. We conclude that there are use cases where the deterioration in performance is outweighed by the value of interpretability.

翻译：在音乐流媒体平台盛行的时代，自动音乐音频标注任务引起了广泛关注，促使研究者设计方法以提升标准数据集上的性能指标。近期大多数方法依赖深度神经网络——尽管这些模型性能卓越，但其黑箱特性使得对给定输入的输出解释面临挑战。尽管可解释性问题在医学等领域已得到重视，但在音乐相关任务中尚未获得关注。本研究探索了自动音乐标注场景中可解释性的价值。我们构建的工作流整合了三种不同信息提取技术：(a) 利用符号知识、(b) 使用辅助深度神经网络、(c) 采用信号处理方法从音频文件中提取感知特征。这些特征随后被用于训练可解释机器学习模型进行标签预测。我们在MTG-Jamendo数据集和GTZAN数据集上开展了实验。所提方法在两个任务中均超越了基线模型性能，并在某些情况下展现出与当前最优模型的竞争力。我们得出结论：在部分应用场景中，性能损失相对于可解释性的价值是可以接受的。