Dataset Open Access
Marius Miron
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nmm##2200000uu#4500</leader> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">source separation</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">classical music</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">neural networks</subfield> </datafield> <controlfield tag="005">20200124192409.0</controlfield> <controlfield tag="001">344499</controlfield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">1112728959</subfield> <subfield code="z">md5:26a2c113f8a31195ed56ba280522192c</subfield> <subfield code="u">https://zenodo.org/record/344499/files/Bach10 Separation SMC2017.tar.gz</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2017-02-28</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire_data</subfield> <subfield code="o">oai:zenodo.org:344499</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Universitat Pompeu Fabra, Barcelona</subfield> <subfield code="a">Marius Miron</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Bach10 Separation SMC2017</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by-nc/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution Non Commercial 4.0 International</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>The Bach10 Separation SMC2017 dataset is derived from the Bach10 dataset, which contains ten pieces of Bach chorales along the scores.<br> We separate the audio files in the original dataset and in the dataset we synthesized with Sibelius (https://zenodo.org/record/321361#.WLW40t-i7J8), using the approaches presented in this paper:<br> Marius Miron, Jordi Janer, Emilia Gomez, "Generating data to train convolutional neural networks for low latency classical music source separation", Sound and Music Computing Conference 2017</p> <p>The dataset contains the separated audio files along the computed measures which give the quality of separation: SDR, SIR, SAR, computed with BSS Eval 3.0. </p> <p>For the intellectual rights and the distribution policy of the original dataset check the Bach10 dataset page:<br> http://music.cs.northwestern.edu/data/Bach10.html</p> <p>The files in Bach10 Separation SMC2017 dataset are offered free of charge for non-commercial use only. You can not redistribute them nor modify them. </p> <p>This dataset is created by Marius Miron, Music Technology Group - Universitat Pompeu Fabra (Barcelona). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License.</p></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.780135</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.344499</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">dataset</subfield> </datafield> </record>
All versions | This version | |
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Views | 230 | 231 |
Downloads | 39 | 39 |
Data volume | 43.4 GB | 43.4 GB |
Unique views | 208 | 209 |
Unique downloads | 34 | 34 |