ChoralSynth: Synthetic Dataset of Choral Singing

TL;DR

ChoralSynth introduces a synthetic choral singing dataset created using synthesizers and public domain scores, aiming to facilitate Music Information Retrieval research by overcoming data scarcity.

Contribution

This work presents a novel methodology for generating high-quality synthetic choral datasets using synthesizers and public domain scores, filling a critical gap in MIR research.

Findings

Dataset includes diverse choral renditions with metadata

Methodology enables scalable and high-quality data creation

Supports new research in singing voice analysis

Abstract

Choral singing, a widely practiced form of ensemble singing, lacks comprehensive datasets in the realm of Music Information Retrieval (MIR) research, due to challenges arising from the requirement to curate multitrack recordings. To address this, we devised a novel methodology, leveraging state-of-the-art synthesizers to create and curate quality renditions. The scores were sourced from Choral Public Domain Library(CPDL). This work is done in collaboration with a diverse team of musicians, software engineers and researchers. The resulting dataset, complete with its associated metadata, and methodology is released as part of this work, opening up new avenues for exploration and advancement in the field of singing voice research.