Thesis Open Access
Pétermann, Darius A,
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">source separation, singing voice, SATB recording, convolutional neural networks, choir music.</subfield> </datafield> <datafield tag="502" ind1=" " ind2=" "> <subfield code="c">Universitat Pompeu Fabra</subfield> </datafield> <controlfield tag="005">20201016002656.0</controlfield> <controlfield tag="001">4091247</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Universitat Pompeu Fabra</subfield> <subfield code="4">ths</subfield> <subfield code="a">Chandna, Pritish</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Universitat Pompeu Fabra</subfield> <subfield code="4">ths</subfield> <subfield code="a">Bonada, Jordi</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">12076163</subfield> <subfield code="z">md5:0d052cb5f9231ccff0caf7a2ffc406c4</subfield> <subfield code="u">https://zenodo.org/record/4091247/files/2020-Darius-Petermann.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2020-09-15</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="p">user-mtgupf</subfield> <subfield code="p">user-smc-master</subfield> <subfield code="o">oai:zenodo.org:4091247</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Universitat Pompeu Fabra</subfield> <subfield code="a">Pétermann, Darius A,</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">SATB Voice Segregation For Monoaural Recordings</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-mtgupf</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-smc-master</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/3.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 3.0 Unported</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>Choral singing is a widely practiced form of ensemble singing wherein a group of people sing simultaneously in polyphonic harmony. The most commonly practiced&nbsp;setting for choir ensembles consists of four parts; Soprano, Alto, Tenor and Bass&nbsp;(SATB), each with its own range of fundamental frequencies (F0s). The task of&nbsp;source separation for this choral setting entails separating the SATB mixture into&nbsp;its constituent parts. Source separation for musical mixtures is well studied and&nbsp;many Deep Learning-based methodologies have been proposed for the same. However,<br> most of the research has been focused on a typical case which consists in<br> separating vocal, percussion and bass sources from a mixture, each of which has a&nbsp;distinct spectral structure. In contrast, the simultaneous and harmonic nature of&nbsp;ensemble singing leads to high structural similarity and overlap between the spectral&nbsp;components of the sources in a choral mixture, making source separation for&nbsp;choirs a harder task than the typical case. This, along with the lack of an appropriate&nbsp;consolidated dataset has led to a dearth of research in the field so far. In&nbsp;this work we first assess how well some of the recently developed methodologies for&nbsp;musical source separation perform for the case of SATB choirs. We then propose a&nbsp;novel domain-specific adaptation for conditioning the recently proposed U-Net architecture<br> for musical source separation using the fundamental frequency contour of<br> each of the singing groups and demonstrate that our proposed approach surpasses&nbsp;results from domain-agnostic architectures. Lastly we assess our approach using&nbsp;different evaluation methodologies, going from objective to subjective-based ones,&nbsp;and provide a comparative analysis of the various results.</p></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.4091246</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.4091247</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">thesis</subfield> </datafield> </record>
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