Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing

Rosenzweig, Sebastian; Cuesta, Helena; Weiß, Christof; Scherbaum, Frank; Müller, Meinard

doi:10.5334/tismir.48

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…Graciarena et al (2003) showed that the information from LMs can improve speech recognition in noisy environments. Askenfelt et al (1980) used LM signals for F0 estimation, which recently received renewed attention in the context of pitch and intonation analysis of Western choir recordings (Rosenzweig et al, 2020). Scherbaum (2016) showed how LM recordings can aid musicological research, e.g.…”

Section: Larynx Microphonesmentioning

confidence: 99%

A Dataset of Larynx Microphone Recordings for Singing Voice Reconstruction

Schwär,

Krause,

Fast

et al. 2024

Transactions of the International Society for Music Information Retrieval

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Larynx microphones (LMs) make it possible to obtain practically crosstalk-free recordings of the human voice by picking up vibrations directly from the throat. This can be useful in a multitude of music information retrieval scenarios related to singing, e.g., the analysis of individual voices recorded in environments with lots of interfering noise. However, LMs have a limited frequency range and barely capture the effects of the vocal tract, which makes the recorded signal unsuitable for downstream tasks that require high-quality recordings. In this paper, we introduce the task of reconstructing a natural sounding, high-quality singing voice recording from an LM signal. With an explicit focus on the singing voice, the problem lies at the intersection of speech enhancement and singing voice synthesis with the additional requirement of faithful reproduction of expressive parameters like intonation. In this context, we make three main contributions. First, we publish a dataset with over 4 hours of popular music we recorded with four amateur singers accompanied by a guitar, where both LM and clean close-up microphone signals are available. Second, we propose a data-driven baseline approach for singing voice reconstruction from LM signals using differentiable signal processing, inspired by a source-filter model that emulates the missing vocal tract effects. Third, we evaluate the baseline with a listening test and further show that it can improve the accuracy of lyrics transcription as an exemplary downstream task.

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Section: Larynx Microphonesmentioning

confidence: 99%

A Dataset of Larynx Microphone Recordings for Singing Voice Reconstruction

Schwär,

Krause,

Fast

et al. 2024

Transactions of the International Society for Music Information Retrieval

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“…In particular, the availability of multitrack datasets has led to impressive results of data-driven MSS approaches that focus on separating popular music recordings. In the Western classical music domain, several datasets have been introduced for polyphonic vocal music (McLeod et al, 2017;Schramm and Benetos, 2017;Cuesta et al, 2018;Rosenzweig et al, 2020), which comprise isolated recordings of vocal ensembles. For instrumental music, however, there are only a few multitrack datasets (see Table 1).…”

Section: Datasetsmentioning

confidence: 99%

Piano Concerto Dataset (PCD): A Multitrack Dataset of Piano Concertos

Özer,

Schwär,

Arifi-Müller

et al. 2023

Transactions of the International Society for Music Information Retrieval

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The piano concerto is a genre of central importance in Western classical music, often consisting of a virtuoso solo part for piano and an orchestral accompaniment. In this article, we introduce the Piano Concerto Dataset (PCD), which comprises a collection of excerpts with separate piano and orchestral tracks from piano concertos ranging from the Baroque to the Post-Romantic era. In particular, using existing backing tracks by the music publisher Music Minus One, we recorded excerpts from 15 different piano concertos played by five interpreters on various instruments under different acoustic conditions. The key challenge of playing along with pre-recorded orchestral accompaniments lies in the exact synchronization of the performer. For guiding the pianists for obtaining a high synchronization accuracy, we used additional click tracks generated with measure and beat annotations of the orchestral tracks, which also are provided in the PCD. The dataset is relevant for a variety of Music Information Retrieval (MIR) tasks, including music source separation, automatic accompaniment, music synchronization, editing, and upmixing.

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“…Similarly, Dagstuhl ChoirSet (DCS) (Rosenzweig et al, 2020) is a dataset with ensemble singing recordings of two songs in Latin and Bulgarian languages. The dataset also contains a set of vocal exercises consisting of scales, cadences, and intonation exercises.…”

Section: Vocal Ensembles Datasetsmentioning

confidence: 99%

A Deep-Learning Based Framework for Source Separation, Analysis, and Synthesis of Choral Ensembles

Chandna

Cuesta

Petermann

2022

Front. Signal Process.

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Choral singing in the soprano, alto, tenor and bass (SATB) format is a widely practiced and studied art form with significant cultural importance. Despite the popularity of the choral setting, it has received little attention in the field of Music Information Retrieval. However, the recent publication of high-quality choral singing datasets as well as recent developments in deep learning based methodologies applied to the field of music and speech processing, have opened new avenues for research in this field. In this paper, we use some of the publicly available choral singing datasets to train and evaluate state-of-the-art source separation algorithms from the speech and music domains for the case of choral singing. Furthermore, we evaluate existing monophonic F0 estimators on the separated unison stems and propose an approximation of the perceived F0 of a unison signal. Additionally, we present a set of applications combining the proposed methodologies, including synthesizing a single singer voice from the unison, and transposing and remixing the separated stems into a synthetic multi-singer choral signal. We finally conduct a set of listening tests to perform a perceptual evaluation of the results we obtain with the proposed methodologies.

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Dagstuhl ChoirSet: A Multitrack Dataset for MIR Research on Choral Singing

Cited by 14 publications

References 22 publications

A Dataset of Larynx Microphone Recordings for Singing Voice Reconstruction

A Dataset of Larynx Microphone Recordings for Singing Voice Reconstruction

Piano Concerto Dataset (PCD): A Multitrack Dataset of Piano Concertos

A Deep-Learning Based Framework for Source Separation, Analysis, and Synthesis of Choral Ensembles

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