2020
DOI: 10.48550/arxiv.2008.07645
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Deep Learning Based Source Separation Applied To Choir Ensembles

Abstract: 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 method… Show more

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“…The U-Net architecture has been extensively used both in audio-only source separation -both on the spectral [19,20,21] and time [22] domains -as well as in its audio-visual counterpart [23,24,25,26,27,28]. We can also find works on source separation that condition the U-Net on prior information such as the presence of certain types of musical instruments [29], phoneme activation for singing voice separation [30] or the fundamental frequency contour of each type of voice sources in choir ensembles [31].…”
Section: Introductionmentioning
confidence: 99%
“…The U-Net architecture has been extensively used both in audio-only source separation -both on the spectral [19,20,21] and time [22] domains -as well as in its audio-visual counterpart [23,24,25,26,27,28]. We can also find works on source separation that condition the U-Net on prior information such as the presence of certain types of musical instruments [29], phoneme activation for singing voice separation [30] or the fundamental frequency contour of each type of voice sources in choir ensembles [31].…”
Section: Introductionmentioning
confidence: 99%