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Evaluating the Genre Classification Performance of Lyrical Features Relative to Audio, Symbolic and Cultural Features.

, , , , , and . ISMIR, page 213-218. International Society for Music Information Retrieval, (2010)

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The Art of Teaching Computers: The SIMSSA Optical Music Recognition Workflow System., and . EUSIPCO, page 1-5. IEEE, (2019)Identifying time zones in a large dataset of music listening logs., and . SoMeRA@SIGIR, page 27-32. ACM, (2014)AUTOMATIC PITCH RECOGNITION IN PRINTED SQUARE-NOTE NOTATION, , , and . (2011)Encoding matters., , and . DLfm, page 69-73. ACM, (2018)A Quantitative Comparison of Position Trackers for the Development of a Touch-less Musical Interface., and . NIME, nime.org, (2012)Creating a large-scale searchable digital collection from printed music materials., , , and . WWW (Companion Volume), page 903-908. ACM, (2012)Creating Latent Spaces for Modern Music Genre Rhythms Using Minimal Training Data., , and . ICCC, page 259-262. Association for Computational Creativity (ACC), (2020)Optical Measure Recognition in Common Music Notation., , and . ISMIR, page 125-130. (2013)Automatic Music Recommendation Systems: Do Demographic, Profiling, and Contextual Features Improve Their Performance?., and . ISMIR, page 94-100. (2016)Automatic Pitch Detection in Printed Square Notation., , , and . ISMIR, page 423-428. University of Miami, (2011)