Abstract
Choral singing is a central part of musical cultures across the world, yet many facets of this widespread form of polyphonic singing are still to be explored. Music information retrieval (MIR) research on choral singing benefits from multitrack recordings of the individual singing voices. However, there exist only few publicly available multitrack datasets on polyphonic singing. In this paper, we present Dagstuhl ChoirSet (DCS), a multitrack dataset of a cappella choral music designed to support MIR research on choral singing. The dataset includes recordings of an amateur vocal ensemble performing two choir pieces in full choir and quartet settings. The audio data was recorded during an MIR seminar at Schloss Dagstuhl using different close-up microphones to capture the individual singers’ voices. In this article, we give detailed insights into all stages of creating DCS: recording process, data preparation, generation of annotations as well as development of suitable interfaces for publicly accessing and reusing the data. Furthermore, we demonstrate the potential of the dataset for MIR research by discussing case studies on choral intonation assessment and multiple fundamental frequency (F0) estimation.
Highlights
Choral singing is one of the most widespread types of polyphonic singing (Sundberg, 1987)
The European Choral Association1 reports over 37 million amateur and professional choir singers on the European continent, while Chorus America2 reports 54 million active singers in the U.S The great interest in choral singing motivates the need for Music information retrieval (MIR) technologies to support singers and conductors in their rehearsal practices (Gómez et al, 2020) via mobile applications3,4 and webbased interfaces
Applications to MIR Research we demonstrate the potential of Dagstuhl ChoirSet (DCS) for MIR research by means of two case studies
Summary
Choral singing is one of the most widespread types of polyphonic singing (Sundberg, 1987). The lack of suitable research data was one of the driving motivations to create Dagstuhl ChoirSet (DCS), a publicly available multitrack dataset of a cappella choral music for MIR research (cf Figure 1). The close-up microphone signals as well as the available F0-trajectories and scores can serve as a baseline to research on (informed) source separation techniques (Cano et al, 2019, 2014). It allows for comparisons between multiple choir/quartet performances, choir settings, and microphone types.
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More From: Transactions of the International Society for Music Information Retrieval
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