2026-04-17T10:14:30Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/457132025-12-18T01:12:02Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Petermann, Darius author Chandna, Pritish author Cuesta, Helena author Bonada, Jordi, 1973- author Gómez Gutiérrez, Emilia, 1975- author 2020-11-11T07:23:58Z 2020-11-11T07:23:58Z 2020 Comunicació presentada a: International Society for Music Information Retrieval Conference celebrat de l'11 al 16 d'octubre de 2020 de manera virtual. Choral singing is a widely practiced form of ensemble singing wherein a group of people sing simultaneously in polyphonic harmony. The most commonly practiced setting for choir ensembles consists of four parts; Soprano, Alto, Tenor and Bass (SATB), each with its own range of fundamental frequencies (F0s). The task of source separation for this choral setting entails separating the SATB mixture into the constituent parts. Source separation for musical mixtures is well studied and many deep learning based methodologies have been proposed for the same. However, most of the research has been focused on a typical case which consists in separating vocal, percussion and bass sources from a mixture, each of which has a distinct spectral structure. In contrast, the simultaneous and harmonic nature of ensemble singing leads to high structural similarity and overlap between the spectral components of the sources in a choral mixture, making source separation for choirs a harder task than the typical case. This, along with the lack of an appropriate consolidated dataset has led to a dearth of research in the field so far. In this paper we first assess how well some of the recently developed methodologies for musical source separation perform for the case of SATB choirs. We then propose a novel domain-specific adaptation for conditioning the recently proposed U-Net architecture for musical source separation using the fundamental frequency contour of each of the singing groups and demonstrate that our proposed approach surpasses results from domain-agnostic architectures. The TITANX used for this research was donated by the NVIDIA Corporation. This work is partially supported by the Towards Richer Online Music Public-domain Archives (TROMPA H2020 770376) project. Helena Cuesta is supported by the FI Predoctoral Grant from AGAUR (Generalitat de Catalunya). The authors would like to thank Rodrigo Schramm and Emmanouil Benetos for sharing their singing voice datasets for this research. Deep learning based source separation applied to choir ensembles