STraDa: A Singer Traits Dataset

TL;DR

This paper introduces STraDa, a large-scale public dataset of music tracks with rich singer metadata, designed to facilitate research in singing voices, bias analysis, and model training.

Contribution

The paper presents STraDa, a novel dataset with extensive metadata and audio files, enabling advanced singing voice research and bias analysis.

Findings

Successful benchmarking of singer sex classification

Demonstrated bias analysis capabilities

Rich metadata supports diverse research applications

Abstract

There is a limited amount of large-scale public datasets that contain downloadable music audio files and rich lead singer metadata. To provide such a dataset to benefit research in singing voices, we created Singer Traits Dataset (STraDa) with two subsets: automatic-strada and annotated-strada. The automatic-strada contains twenty-five thousand tracks across numerous genres and languages of more than five thousand unique lead singers, which includes cross-validated lead singer metadata as well as other track metadata. The annotated-strada consists of two hundred tracks that are balanced in terms of 2 genders, 5 languages, and 4 age groups. To show its use for model training and bias analysis thanks to its metadata's richness and downloadable audio files, we benchmarked singer sex classification (SSC) and conducted bias analysis.