DiVa: An Iterative Framework to Harvest More Diverse and Valid Labels from User Comments for Music

TL;DR

This paper introduces DiVa, an iterative framework that leverages user comments to automatically harvest more diverse and valid music labels, addressing the limitations of existing methods that lack label diversity.

Contribution

DiVa is a novel iterative approach that uses pseudo-labels and a joint score function to extract diverse labels from user comments for music annotation.

Findings

DiVa outperforms state-of-the-art solutions in label diversity.

The framework effectively captures labels missed by gold annotations.

Experimental results demonstrate the superiority of DiVa on a densely annotated dataset.

Abstract

Towards sufficient music searching, it is vital to form a complete set of labels for each song. However, current solutions fail to resolve it as they cannot produce diverse enough mappings to make up for the information missed by the gold labels. Based on the observation that such missing information may already be presented in user comments, we propose to study the automated music labeling in an essential but under-explored setting, where the model is required to harvest more diverse and valid labels from the users' comments given limited gold labels. To this end, we design an iterative framework (DiVa) to harvest more $\underline{\text{Di}}$verse and $\underline{\text{Va}}$lid labels from user comments for music. The framework makes a classifier able to form complete sets of labels for songs via pseudo-labels inferred from pre-trained classifiers and a novel joint score function. The experiment on a densely annotated testing set reveals the superiority of the Diva over state-of-the-art solutions in producing more diverse labels missed by the gold labels. We hope our work can inspire future research on automated music labeling.