Using Note-Level Music Encodings to Facilitate Interdisciplinary Research on Human Engagement with Music
By: Johanna Devaney
References
- Aljanaki, A., Yang, Y.-H., & Soleymani, M. (2017). Developing a benchmark for emotional analysis of music. PLoS ONE, 12(3). DOI: 10.1371/journal.pone.0173392
- Baratè, A., Ludovico, L. A., Simonetta, F., & Mauro, D. A. (2019). On the adoption of standard encoding formats to ensure interoperability of music digital archives: The IEEE 1599 format. In Proceedings of the 6th International Conference on Digital Libraries for Musicology (DLfM), pages 20–24. DOI: 10.1145/3358664.3358665
- Benetos, E., Dixon, S., Giannoulis, D., Kirchhoff, H., & Klapuri, A. (2013). Automatic music transcription: Challenges and future directions. Journal of Intelligent Information Systems, 41(3), 407–34. DOI: 10.1007/s10844-013-0258-3
- Blanco, A. D., & Ramirez, R. (2019). Evaluation of a sound quality visual feedback system for bow learning technique in violin beginners: An EEG study. Frontiers in Psychology, 10, 165. DOI: 10.3389/fpsyg.2019.00165
- Bonardi, A. (2000). IR for contemporary music: What the musicologist needs. In Proceedings of the International Symposium on Music Information Retrieval (ISMIR).
- Brittin, R. V. (1995). Comparing continuous versus static measurements in music listeners’ preferences. Journal of Research in Music Education, 43(1), 36–46. DOI: 10.2307/3345790
- Brook, B. S. (1965). The simplified “Plaine and Easie Code System” for notating music: A proposal for international adoption. Fontes artis musicae, 12(2/3), 156–60.
- Brown, A. R. (1999). An introduction to music analysis with computer. XArt Online Journal, 4(1).
- Broze, Y., & Shanahan, D. (2013). Diachronic changes in jazz harmony: A cognitive perspective. Music Perception: An Interdisciplinary Journal, 31(1), 32–45. DOI: 10.1525/mp.2013.31.1.32
- Burgoyne, J. A., Wild, J., & Fujinaga, I. (2011). An expert ground truth set for audio chord recognition and music analysis. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 633–638.
- Cancino-Chacón, C. E., Grachten, M., Goebl, W., & Widmer, G. (2018). Computational models of expressive music performance: A comprehensive and critical review. Frontiers in Digital Humanities, 5, 25. DOI: 10.3389/fdigh.2018.00025
- Cannam, C., Landone, C., Sandler, M. B., & Bello, J. P. (2006). The Sonic Visualiser: A visualization platform for semantic descriptors from musical signals. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 324–7.
- Chen, A. L. (2000). Music representation, indexing and retrieval at NTHU. In Proceedings of the International Symposium on Music Information Retrieval (ISMIR).
- Chen, T.-P., & Su, L. (2018). Functional harmony recognition of symbolic music data with multi-task recurrent neural networks. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 90–97.
- Condit-Schultz, N., Ju, Y., & Fujinaga, I. (2018). A flexible approach to automated harmonic analysis: Multiple annotations of chorales by Bach and Prætorius. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 66–73.
- Cook, N. (2013). Beyond the score: Music as performance. Oxford University Press. DOI: 10.1093/acprof:oso/9780199357406.001.0001
- Cuthbert, M. S., & Ariza, C. (2010). music21: A toolkit for computer-aided musicology and symbolic music data. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 637–642.
- Dai, J., Mauch, M., & Dixon, S. (2015). Analysis of intonation trajectories in solo singing. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 420–6.
- Dannenberg, R. B. (1993). A brief survey of music representation issues, techniques, and systems. Computer Music Journal, 17(3), 20–30. DOI: 10.2307/3680940
- De Roure, D., Klyne, G., Pybus, J., Weigl, D. M., & Page, K. (2018). Music SOFA: An architecture for semantically informed recomposition of digital music objects. In Proceedings of the 1st InternationalWorkshop on Semantic Applications for Audio and Music, pages 33–41. DOI: 10.1145/3243907.3243912
- Devaney, J., & Arthur, C. (2015). Developing a structurally significant representation of musical audio through domain knowledge. In International Society for Music Information Retrieval (ISMIR) Conference, Late-Breaking Demo.
- Devaney, J., Arthur, C., Condit-Schultz, N., & Nisula, K. (2015). Theme And Variation Encodings with Roman Numerals (TAVERN): A new data set for symbolic music analysis. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 728–34.
- Devaney, J., & Gauvin, H. L. (2019). Encoding music performance data in Humdrum and MEI. International Journal on Digital Libraries, 20(1), 81–91. DOI: 10.1007/s00799-017-0229-3
- Devaney, J., Mandel, M. I., & Fujinaga, I. (2012). A study of intonation in three-part singing using the Automatic Music Performance Analysis and Comparison Toolkit (AMPACT). In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 511–516.
- Devaney, J., & Shanahan, D. (2014). Evaluating ruleand exemplar-based computational approaches to modeling harmonic function in music theory pedagogy. In Proceedings of the 9th Conference on Interdisciplinary Musicology.
- Erickson, R. F. (1970). Music and the computer in the sixties. In Proceedings of the May 5–7, 1970, Spring Joint Computer Conference, pages 281–285. DOI: 10.1145/1476936.1476983
- Erickson, R. F. (1975). “the DARMS project”: A status report. Computers and the Humanities, 9(6), 291–298. DOI: 10.1007/BF02396292
- Fields, B., Page, K., De Roure, D., & Crawford, T. (2011). The segment ontology: Bridging musicgeneric and domain-specific. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 1–6.
IEEE . DOI: 10.1109/ICME.2011.6012204 - Futrelle, J., & Downie, J. S. (2003). Interdisciplinary research issues in music information retrieval: ISMIR 2000–2002. Journal of New Music Research, 32(2), 121–131. DOI: 10.1076/jnmr.32.2.121.16740
- Geekie, G. (2002). Carnatic ragas as music information retrieval entities. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 257–8.
- Gingras, B., & McAdams, S. (2011). Improved scoreperformance matching using both structural and temporal information from MIDI recordings. Journal of New Music Research, 40(1), 43–57. DOI: 10.1080/09298215.2010.545422
- Giraud, M., Groult, R., & Leguy, E. (2018). Dezrann, a web framework to share music analysis. In Proceedings of the International Conference on Technologies for Music Notation and Representation (TENOR), pages 104–110.
- Goebl, W., Dixon, S., & Schubert, E. (2014).
Quantitative methods: Motion analysis, audio analysis, and continuous response techniques . In Fabian, D., Timmers, R., and Schubert, E., editors, Expressiveness in Music Performance: Empirical Approaches across Styles and Cultures, pages 221–239. Oxford University Press, Oxford. DOI: 10.1093/acprof:oso/9780199659647.003.0013 - Good, M. (2000). Representing music using XML. In Proceedings of the International Symposium on Music Information Retrieval (ISMIR).
- Gotham, M., & Ireland, M. (2019). Taking form: A representation standard, conversion code, and example corpus for recording, visualizing, and studying analyses of musical form. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference.
- Goto, M., Hashiguchi, H., Nishimura, T., & Oka, R. (2002). RWC Music Database: Popular, classical and jazz music databases. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 287–8.
- Hamanaka, M., Hirata, K., & Tojo, S. (2014). Musical structural analysis database based on GTTM. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 325–30.
- Han, B.-j., Rho, S., Dannenberg, R. B., & Hwang, E. (2009). SMERS: Music emotion recognition using support vector regression. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 651–6.
- Hankinson, A., Roland, P., & Fujinaga, I. (2011). The Music Encoding Initiative as a document-encoding framework. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 293–8.
- Harte, C., Sandler, M. B., Abdallah, S. A., & Gómez, E. (2005). Symbolic representation of musical chords: A proposed syntax for text annotations. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 66–71.
- Hewlett, W. B. (1997).
MuseData: Multipurpose representation . In Selfridge-Field, E., editor, Beyond MIDI: The Handbook of Musical Codes, pages 402–47. MIT Press, Cambridge, MA. - Hu, Y., Weigl, D. M., Page, K. R., Dubnicek, R., & Downie, J. S. (2019). Bridging the information gap between structural and note-level musical datasets. In Proceedings of iConference 2019.
iSchools . DOI: 10.21900/iconf.2019.103320 - Huang, J., & Lerch, A. (2019). Automatic assessment of sight-reading exercises. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 581–587.
- Huron, D. B. (1994). The Humdrum Toolkit: Reference Manual. Center for Computer Assisted Research in the Humanities.
- Jeong, D., Kwon, T., Park, C., & Nam, J. (2017). Performscore: Toward performance visualization with the score on the web browser. In International Society for Music Information Retrieval (ISMIR) Conference, Late-Breaking Demo.
- Kaliakatsos-Papakostas, M. A., Zacharakis, A. I., Tsougras, C., & Cambouropoulos, E. (2015). Evaluating the general chord type representation in tonal music and organising GCT chord labels in functional chord categories. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 427–33.
- Kaneko, H., Kawakami, D., & Sagayama, S. (2010). Functional harmony annotation database for statistical music analysis. In International Society for Music Information Retrieval (ISMIR) Conference: Late-Breaking Session.
- Kirke, A., & Miranda, E. R. (2012). Guide to Computing for Expressive Music Performance. Springer Science & Business Media. DOI: 10.1007/978-1-4471-4123-5
- Kirlin, P. B. (2014). A data set for computational studies of Schenkerian analysis. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 213–18.
- Knopke, I. (2008). The Perlhumdrum and Perllilypond toolkits for symbolic music information retrieval. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 147–52.
- Kornstädt, A. (2001). The jRing system for computerassisted musicological analysis. In International Symposium on Music Information Retrieval (ISMIR), pages 93–8.
- Kosta, K., Bandtlow, O. F., & Chew, E. (2018). MazurkaBL: Score-aligned loudness, beat, expressive markings data for 2000 Chopin mazurka recordings. In Proceedings of the 4th International Conference on Technologies for Music Notation and Representation (TENOR), pages 85–94.
- Kuuskankare, M., & Laurson, M. (2004). Expressive Notation Package - an overview. In International Conference on Music Information Retrieval (ISMIR).
- Kuuskankare, M., & Sapp, C. (2013). Visual Humdrum-library for PWGL. In International Society for Music Information Retrieval (ISMIR) Conference.
- Lee, J. H., Downie, J. S., & Renear, A. (2002). Representing Korean traditional musical notation in XML. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 263–4.
- Lerdahl, F., & Jackendoff, R. (1983). A Generative Theory of Tonal Music (GTTM). MIT Press.
- Lewis, D., Weigl, D., Bullivant, J., & Page, K. (2018). Publishing musicology using multimedia digital libraries: Creating interactive articles through a framework for linked data and MEI. In Proceedings of the 5th International Conference on Digital Libraries for Musicology (DLfM), pages 21–5. DOI: 10.1145/3273024.3273038
- Loy, G. (1985). Musicians make a standard: The MIDI phenomenon. Computer Music Journal, 9(4), 8–26. DOI: 10.2307/3679619
- MacLellan, D., & Boehm, C. (2000). MuTaTeD’ll: A system for music information retrieval of encoded music. In Proceedings of the International Symposium on Music Information Retrieval (ISMIR).
- Madsen, C. K. (1997). Emotional response to music. Psychomusicology: A Journal of Research in Music Cognition, 16(1–2), 59. DOI: 10.1037/h0094067
- Marchini, M., Ramirez, R., Papiotis, P., & Maestre, E. (2014). The sense of ensemble: A machine learning approach to expressive performance modeling in string quartets. Journal of New Music Research, 43(3), 303–17. DOI: 10.1080/09298215.2014.922999
- Mayor, O., Bonada, J., & Loscos, A. (2009). Performance analysis and scoring of the singing voice. In Proceedings of the 35th Audio Engineering Society (AES) International Conference, pages 1–7.
- McFee, B., & Bello, J. P. (2017). Structured training for large-vocabulary chord recognition. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 188–94.
- McFee, B., Kim, J. W., Cartwright, M., Salamon, J., Bittner, R. M., & Bello, J. P. (2018). Opensource practices for music signal processing research: Recommendations for transparent, sustainable, and reproducible audio research. IEEE Signal Processing Magazine, 36(1), 128–137. DOI: 10.1109/MSP.2018.2875349
- Meroño-Peñuela, A., Hoekstra, R., Gangemi, A., Bloem, P., de Valk, R., Stringer, B., Janssen, B., de Boer, V., Allik, A., Schlobach, S., & Page, K. (2017). The MIDI linked data cloud. In Proceedings of the International Semantic Web Conference, pages 156–164.
Springer . DOI: 10.1007/978-3-319-68204-4_16 - Meroño-Peñuela, A., Kent-Muller, A., de Valk, R., Daquino, M., & Daga, E. (2018). A large-scale semantic library of MIDI linked data. In Proceedings of the 5th International Conference on Digital Libraries for Musicology (DLfM).
- Moog, R. A. (1986). MIDI: Musical instrument digital interface. Journal of the Audio Engineering Society, 34(5), 394–404.
- Nakamura, E., Yoshii, K., & Katayose, H. (2017). Performance error detection and post-processing for fast and accurate symbolic music alignment. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 347–353.
- Nápoles, N., Vigliensoni, G., & Fujinaga, I. (2018). Encoding matters. In Proceedings of the 5th International Conference on Digital Libraries for Musicology (DLfM), pages 69–73. DOI: 10.1145/3273024.3273027
- Neuwirth, M., Harasim, D., Moss, F. C., & Rohrmeier, M. (2018). The Annotated Beethoven Corpus (ABC): A dataset of harmonic analyses of all Beethoven string quartets. Frontiers in Digital Humanities, 5, 16. DOI: 10.3389/fdigh.2018.00016
- Olson, T., & Downie, J. S. (2003). Chopin early editions: Construction and usage of online digital scores. In Proceedings of the International Conference on Music Information Retrieval (ISMIR).
- Page, K., Lewis, D., & Weigl, D. (2019). MELD: A linked data framework for multimedia access to music digital libraries. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 434–5.
IEEE . DOI: 10.1109/JCDL.2019.00106 - Page, K., Nurmikko-Fuller, T., Rindfleisch, C., Lewis, R., Dreyfus, L., & De Roure, D. D. (2015). A toolkit for live annotation of opera performance: Experiences capturing Wagner’s Ring Cycle. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 211–7.
- Peeters, G., & Assayag, G. (2002). Tutorial on digital music representations. In Proceedings of the International Conference on Music Information Retrieval (ISMIR).
- Politis, D., & Linardis, P. (2001). Musical information retrieval for delta and neumatic systems. In Proceedings of the International Symposium on Music Information Retrieval (ISMIR), pages 23–4.
- Pugin, L., Kepper, J., Roland, P., Hartwig, M., & Hankinson, A. (2012). Separating presentation and content in MEI. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 505–10.
- Pugin, L., Zitellini, R., & Roland, P. (2014). Verovio: A library for engraving MEI music notation into SVG. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 107–12.
- Rizo, D., & Marsden, A. (2019). An MEI-based standard encoding for hierarchical music analyses. International Journal on Digital Libraries, 20(1): 93–105. DOI: 10.1007/s00799-018-0262-x
- Rohrmeier, M., & Cross, I. (2008). Statistical properties of tonal harmony in Bach’s chorales. In Proceedings of the 10th International Conference on Music Perception and Cognition.
- Roland, P. (2002). The Music Encoding Initiative (MEI). In Proceedings of the First International Conference on Musical Applications Using XML, pages 55–9.
- Sapp, C. S. (2005). Online database of scores in the Humdrum file format. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 664–5.
- Sapp, C. S. (2007). Comparative analysis of multiple musical performances. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 497–500.
- Schedl, M., Hauger, D., Tkalčič, M., Melenhorst, M., & Liem, C. C. (2016). A dataset of multimedia material about classical music: PHENICX-SMM. In Proceedings of the 14th International Workshop on Content-Based Multimedia Indexing (CBMI), pages 1–4.
IEEE . DOI: 10.1109/CBMI.2016.7500240 - Schmalfeldt, J. (1985). On the relation of analysis to performance: Beethoven’s “Bagatelles” op. 126, nos. 2 and 5. Journal of Music Theory, 29(1), 1–31. DOI: 10.2307/843369
- Schmidt, E. M., & Kim, Y. E. (2010). Prediction of time-varying musical mood distributions from audio. In Proceedings of the International Society for Music Information Retrieval (ISMIR) Conference, pages 465–70. DOI: 10.1109/ICMLA.2010.101
- Selfridge-Field, E. (1997). Beyond MIDI. MIT Press, Cambridge, MA.
- Selfridge-Field, E., & Sapp, C. (2008). Survey of symbolic data for music applications tutorial. In Proceedings of the International Conference on Music Information Retrieval (ISMIR).
- Temperley, D. (2009). A unified probabilistic model for polyphonic music analysis. Journal of New Music Research, 38(1), 3–18. DOI: 10.1080/09298210902928495
- Timmers, R. (2007). Vocal expression in recorded performances of Schubert songs. Musicae Scientiae, 11(2), 237–68. DOI: 10.1177/102986490701100205
- Upham, F. (2018). Detecting the Adaptation of Listeners’ Respiration to Heard Music. PhD thesis, New York University.
- Weigl, D., Goebl, W., Crawford, T., Gkiokas, A. F., Gutierrez, N., Porter, A., Santos, P., Karreman, C., Vroomen, I., CS Liem, C., Sarasúa, A., & van Tilburg, M. (2019a). Interweaving and enriching digital music collections for scholarship, performance, and enjoyment. In Proceedings of the 6th International Conference on Digital Libraries for Musicology (DLfM), pages 84–88.
- Weigl, D., & Page, K. (2017). A framework for distributed semantic annotation of musical score: “Take it to the bridge!”. In Proceedings of the International Society for Music Information Retrieval Conference, pages 221–8.
- Weigl, D. M., Lewis, D., Crawford, T., Knopke, I., & Page, K. R. (2019b). On providing semantic alignment and unified access to music library metadata. International Journal on Digital Libraries, 20(1), 25–47. DOI: 10.1007/s00799-017-0223-9
- White, C. W., & Quinn, I. (2018). Chord context and harmonic function in tonal music. Music Theory Spectrum, 40(2), 314–335. DOI: 10.1093/mts/mty021
- Wolff, A. B. (1977). Problems of representation in musical computing. Computers and the Humanities, 11(1), 3–12. DOI: 10.1007/BF02401448
DOI: https://doi.org/10.5334/tismir.56 | Journal eISSN: 2514-3298
Language: English
Submitted on: Feb 29, 2020
Accepted on: Sep 9, 2020
Published on: Oct 26, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2020 Johanna Devaney, published by Ubiquity Press
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