Evaluation of CNN-based Automatic Music Tagging Models
Published in
arXiv.org(2020)External Links:
TL
TL;DR
A consistent evaluation of different music tagging models on three datasets is conducted and reference results using common evaluation metrics are provided and all the models are evaluated with perturbed inputs to investigate the generalization capabilities concerning time stretch, pitch shift, dynamic range compression, and addition of white noise.
Abstract
Recent advances in deep learning accelerated the development of content-based automatic music tagging systems. Music information retrieval (MIR) researchers proposed various architecture designs, mainly based on convolutional neural networks (CNNs), that achieve state-of-the-art results in this multi-label binary classification task. However, due to the differences in experimental setups followed by researchers, such as using different dataset splits and software versions for evaluation, it is difficult to compare the proposed architectures directly with each other. To facilitate further research, in this paper we conduct a consistent evaluation of different music tagging models on three datasets (MagnaTagATune, Million Song Dataset, and MTG-Jamendo) and provide reference results using common evaluation metrics (ROC-AUC and PR-AUC). Furthermore, all the models are evaluated with perturbed inputs to investigate the generalization capabilities concerning time stretch, pitch shift, dynamic range compression, and addition of white noise. For reproducibility, we provide the PyTorch implementations with the pre-trained models.
References24 items
1
Data-Driven Harmonic Filters for Audio Representation Learning
2
Toward Interpretable Music Tagging with Self-Attention
3
Adaptive Pooling Operators for Weakly Labeled Sound Event Detection
4
End-to-end Learning for Music Audio Tagging at Scale
5