International Audio Laboratories Erlangen

Dagstuhl ChoirSet

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1. Sebastian Rosenzweig, Helena Cuesta, Christof Weiß, Frank Scherbaum, Emilia Gómez, and Meinard Müller
   Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing
   Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1): 98–110, 2020. Bibtex PDF Demo DOI

   @article{RosenzweigCWSGM20_DCS_TISMIR,
   author    = {Sebastian Rosenzweig and Helena Cuesta and Christof Wei{\ss} and Frank Scherbaum and Emilia G{\'o}mez and Meinard M{\"u}ller},
   title     = {{D}agstuhl {ChoirSet}: {A} Multitrack Dataset for {MIR} Research on Choral Singing},
   journal   = {Transactions of the International Society for Music Information Retrieval ({TISMIR})},
   volume    = {3},
   number    = {1},
   year      = {2020},
   pages     = {98--110},
   publisher = {Ubiquity Press},
   doi       = {10.5334/tismir.48},
   url       = {http://doi.org/10.5334/tismir.48},
   url-pdf   = {2020_RosenzweigCWSGM_DagstuhlChoirSet_TISMIR_ePrint.pdf},
   url-demo  = {https://www.audiolabs-erlangen.de/resources/MIR/2020-DagstuhlChoirSet}
   }

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.

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If you publish results obtained using this dataset, please cite [1].

Dagstuhl ChoirSet (External Link)
DCStoolbox (External Link)
mirdata (External Link)

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Systematic Exercises & Additional Recordings                Search: 

Acknowledgments

We would like to thank all participants of the Dagstuhl seminar on “Computational Methods for Melody and Voice Processing in Music Recordings” for contributing to the recordings. Furthermore, we thank Schloss Dagstuhl for hosting the seminar and for supporting the recording session. We thank Vlora Arifi-Müller, Simon Schwär and Moritz Berendes for helping with the annotations and Florian Schuberth for helping with Python implementations. Sebastian Rosenzweig is supported by the German Research Foundation (DFG MU 2686/12-1). Helena Cuesta is supported by FI Predoctoral Grant from AGAUR (Generalitat de Catalunya) and by the European Commission under the TROMPA project (H2020 770376). The International Audio Laboratories Erlangen are a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS.