Thesis Open Access

SATB Voice Segregation For Monoaural Recordings

Pétermann, Darius A,


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    <dct:title>SATB Voice Segregation For Monoaural Recordings</dct:title>
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    <dcat:keyword>source separation, singing voice, SATB recording, convolutional neural networks, choir music.</dcat:keyword>
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    <dct:description>&lt;p&gt;Choral singing is a widely practiced form of ensemble singing wherein a group of people sing simultaneously in polyphonic harmony. The most commonly practiced&amp;nbsp;setting for choir ensembles consists of four parts; Soprano, Alto, Tenor and Bass&amp;nbsp;(SATB), each with its own range of fundamental frequencies (F0s). The task of&amp;nbsp;source separation for this choral setting entails separating the SATB mixture into&amp;nbsp;its constituent parts. Source separation for musical mixtures is well studied and&amp;nbsp;many Deep Learning-based methodologies have been proposed for the same. However,&lt;br&gt; most of the research has been focused on a typical case which consists in&lt;br&gt; separating vocal, percussion and bass sources from a mixture, each of which has a&amp;nbsp;distinct spectral structure. In contrast, the simultaneous and harmonic nature of&amp;nbsp;ensemble singing leads to high structural similarity and overlap between the spectral&amp;nbsp;components of the sources in a choral mixture, making source separation for&amp;nbsp;choirs a harder task than the typical case. This, along with the lack of an appropriate&amp;nbsp;consolidated dataset has led to a dearth of research in the field so far. In&amp;nbsp;this work we first assess how well some of the recently developed methodologies for&amp;nbsp;musical source separation perform for the case of SATB choirs. We then propose a&amp;nbsp;novel domain-specific adaptation for conditioning the recently proposed U-Net architecture&lt;br&gt; for musical source separation using the fundamental frequency contour of&lt;br&gt; each of the singing groups and demonstrate that our proposed approach surpasses&amp;nbsp;results from domain-agnostic architectures. Lastly we assess our approach using&amp;nbsp;different evaluation methodologies, going from objective to subjective-based ones,&amp;nbsp;and provide a comparative analysis of the various results.&lt;/p&gt;</dct:description>
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