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Butter Lyrics Over Hominy Grit: Comparing Audio and Psychology-Based Text Features in MIR Tasks.

, , , , and . ISMIR, page 861-868. (2020)

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Annotation Practices in Societally Impactful Machine Learning Applications: What are Popular Recommender Systems Models Actually Trained On?, , and . Perspectives@RecSys, volume 3476 of CEUR Workshop Proceedings, CEUR-WS.org, (2023)Treat societally impactful scientific insights as open-source software artifacts., and . ICSE (SEIS), page 150-156. IEEE, (2023)Go with the Flow: When Listeners Use Music as Technology., , and . ISMIR, page 292-298. (2016)An Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the Effort to Automate the Understanding of Vocals in Music., , , , , , , , , and 2 other author(s). IEEE Signal Process. Mag., 36 (1): 82-94 (2019)Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference., , , and . UMAP, page 285-293. ACM, (2019)Butter Lyrics Over Hominy Grit: Comparing Audio and Psychology-Based Text Features in MIR Tasks., , , , and . ISMIR, page 861-868. (2020)Algorithms Aside: Recommendation As The Lens Of Life., , , , , , , , , and 10 other author(s). RecSys, page 215-219. ACM, (2016)Vocals in Music Matter: the Relevance of Vocals in the Minds of Listeners., , , and . ISMIR, page 514-520. (2018)