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.

在音乐流媒体平台时代，自动标记音乐音频的任务引起了广泛关注，驱使研究人员设计旨在提高标准数据集上性能指标的方法。本研究探讨了可解释性在自动音乐标记领域的相关性，构建了一个包含三种不同信息提取技术的工作流程，并使用这些特征训练了一个可解释的机器学习模型进行标签预测。通过在 MTG-Jamendo 数据集和 GTZAN 数据集上进行实验，我们的方法在两个任务中均超过了基线模型的性能，并在某些情况下展现出与当前最先进方法的竞争力。我们得出结论，在某些情况下，解释性的价值超过了性能的下降。

可解释音频标签的感知音乐特征

Perceptual Musical Features for Interpretable Audio Tagging

We present a content-based automatic music tagging algorithm using fully
convolutional neural networks (FCNs). We evaluate different architectures
consisting of 2D convolutional layers and subsampling layers only. In the
experiments, we measure the AUC-ROC scores of the architectures with different
complexities and input types using the MagnaTagATune dataset, where a 4-layer
architecture shows state-of-the-art performance with mel-spectrogram input.
Furthermore, we evaluated the performances of the architectures with varying
the number of layers on a larger dataset (Million Song Dataset), and found that
deeper models outperformed the 4-layer architecture. The experiments show that
mel-spectrogram is an effective time-frequency representation for automatic
tagging and that more complex models benefit from more training data.

本篇论文采用全卷积神经网络实现一种基于内容的自动音乐标记算法，通过在 MagnaTagATune 数据集上的实验，我们发现采用 mel-spectrogram 作为输入，4 层结构的全卷积神经网络在 AUC-ROC 分数方面达到了最先进的性能，但在 Million Song 数据集上则表明，更深的模型优于 4 层结构。实验证明 mel-spectrogram 是一种有效的时频表示方法，更复杂的模型能够从更多的训练数据中受益。