@Misc{             cancino-chacon.ea2022-partitura,
    author       = {Cancino-Chac{\'{o}}n, Carlos and Peter, Silvan David and
                   Karystinaios, Emmanouil and Foscarin, Francesco and
                   Grachten, Maarten and Widmer, Gerhard},
    year         = {2022},
    title        = {Partitura: {A} {Python} {Package} for {Symbolic} {Music}
                   {Processing}},
    shorttitle   = {Partitura},
    url          = {http://arxiv.org/abs/2206.01071},
    abstract     = {Partitura is a lightweight Python package for handling
                   symbolic musical information. It provides easy access to
                   features commonly used in music information retrieval
                   tasks, like note arrays (lists of timed pitched events)
                   and 2D piano roll matrices, as well as other score
                   elements such as time and key signatures, performance
                   directives, and repeat structures. Partitura can load
                   musical scores (in MEI, MusicXML, Humdrum **kern, and MIDI
                   formats), MIDI performances, and score-to-performance
                   alignments. The package includes some tools for music
                   analysis, such as automatic pitch spelling, key signature
                   identification, and voice separation. Partitura is an
                   open-source project and is available at
                   https://github.com/CPJKU/partitura/.},
    language     = {en},
    urldate      = {2022-07-31},
    publisher    = {arXiv},
    month        = jun,
    note         = {arXiv:2206.01071 [cs, eess]},
    tags         = {music and computer},
    keywords     = {Computer Science - Digital Libraries, Computer Science -
                   Sound, Electrical Engineering and Systems Science - Audio
                   and Speech Processing}
}

{"_id":"HT95iaJmjcsWCrzns","bibbaseid":"cancinochacn-peter-karystinaios-foscarin-grachten-widmer-partituraapythonpackageforsymbolicmusicprocessing-2022","author_short":["Cancino-Chacón, C.","Peter, S. D.","Karystinaios, E.","Foscarin, F.","Grachten, M.","Widmer, G."],"bibdata":{"bibtype":"misc","type":"misc","author":[{"propositions":[],"lastnames":["Cancino-Chacón"],"firstnames":["Carlos"],"suffixes":[]},{"propositions":[],"lastnames":["Peter"],"firstnames":["Silvan","David"],"suffixes":[]},{"propositions":[],"lastnames":["Karystinaios"],"firstnames":["Emmanouil"],"suffixes":[]},{"propositions":[],"lastnames":["Foscarin"],"firstnames":["Francesco"],"suffixes":[]},{"propositions":[],"lastnames":["Grachten"],"firstnames":["Maarten"],"suffixes":[]},{"propositions":[],"lastnames":["Widmer"],"firstnames":["Gerhard"],"suffixes":[]}],"year":"2022","title":"Partitura: A Python Package for Symbolic Music Processing","shorttitle":"Partitura","url":"http://arxiv.org/abs/2206.01071","abstract":"Partitura is a lightweight Python package for handling symbolic musical information. It provides easy access to features commonly used in music information retrieval tasks, like note arrays (lists of timed pitched events) and 2D piano roll matrices, as well as other score elements such as time and key signatures, performance directives, and repeat structures. Partitura can load musical scores (in MEI, MusicXML, Humdrum **kern, and MIDI formats), MIDI performances, and score-to-performance alignments. The package includes some tools for music analysis, such as automatic pitch spelling, key signature identification, and voice separation. Partitura is an open-source project and is available at https://github.com/CPJKU/partitura/.","language":"en","urldate":"2022-07-31","publisher":"arXiv","month":"June","note":"arXiv:2206.01071 [cs, eess]","tags":"music and computer","keywords":"Computer Science - Digital Libraries, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing","bibtex":"@Misc{ cancino-chacon.ea2022-partitura,\n author = {Cancino-Chac{\\'{o}}n, Carlos and Peter, Silvan David and\n Karystinaios, Emmanouil and Foscarin, Francesco and\n Grachten, Maarten and Widmer, Gerhard},\n year = {2022},\n title = {Partitura: {A} {Python} {Package} for {Symbolic} {Music}\n {Processing}},\n shorttitle = {Partitura},\n url = {http://arxiv.org/abs/2206.01071},\n abstract = {Partitura is a lightweight Python package for handling\n symbolic musical information. It provides easy access to\n features commonly used in music information retrieval\n tasks, like note arrays (lists of timed pitched events)\n and 2D piano roll matrices, as well as other score\n elements such as time and key signatures, performance\n directives, and repeat structures. Partitura can load\n musical scores (in MEI, MusicXML, Humdrum **kern, and MIDI\n formats), MIDI performances, and score-to-performance\n alignments. The package includes some tools for music\n analysis, such as automatic pitch spelling, key signature\n identification, and voice separation. Partitura is an\n open-source project and is available at\n https://github.com/CPJKU/partitura/.},\n language = {en},\n urldate = {2022-07-31},\n publisher = {arXiv},\n month = jun,\n note = {arXiv:2206.01071 [cs, eess]},\n tags = {music and computer},\n keywords = {Computer Science - Digital Libraries, Computer Science -\n Sound, Electrical Engineering and Systems Science - Audio\n and Speech Processing}\n}\n\n","author_short":["Cancino-Chacón, C.","Peter, S. D.","Karystinaios, E.","Foscarin, F.","Grachten, M.","Widmer, G."],"key":"cancino-chacon.ea2022-partitura","id":"cancino-chacon.ea2022-partitura","bibbaseid":"cancinochacn-peter-karystinaios-foscarin-grachten-widmer-partituraapythonpackageforsymbolicmusicprocessing-2022","role":"author","urls":{"Paper":"http://arxiv.org/abs/2206.01071"},"keyword":["Computer Science - Digital Libraries","Computer Science - Sound","Electrical Engineering and Systems Science - Audio and Speech Processing"],"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"misc","biburl":"https://hmb.sampaio.me/bibliografia.bib.txt","dataSources":["n6MFY2CscQLDpJ7nT"],"keywords":["computer science - digital libraries","computer science - sound","electrical engineering and systems science - audio and speech processing"],"search_terms":["partitura","python","package","symbolic","music","processing","cancino-chacón","peter","karystinaios","foscarin","grachten","widmer"],"title":"Partitura: A Python Package for Symbolic Music Processing","year":2022,"downloads":1}