Deep Learning Based Source Separation Applied To Choir Ensembles

by   Darius Petermann, et al.

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.


page 1

page 2

page 3

page 4


Unsupervised Audio Source Separation Using Differentiable Parametric Source Models

Supervised deep learning approaches to underdetermined audio source sepa...

Moisesdb: A dataset for source separation beyond 4-stems

In this paper, we introduce the MoisesDB dataset for musical source sepa...

A Deep Learning Based Analysis-Synthesis Framework For Unison Singing

Unison singing is the name given to an ensemble of singers simultaneousl...

Drum-Aware Ensemble Architecture for Improved Joint Musical Beat and Downbeat Tracking

This paper presents a novel system architecture that integrates blind so...

Content Based Singing Voice Extraction From a Musical Mixture

We present a deep learning based methodology for extracting the singing ...

Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision

Most music source separation systems require large collections of isolat...

A Style Transfer Approach to Source Separation

Training neural networks for source separation involves presenting a mix...

Please sign up or login with your details

Forgot password? Click here to reset