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Audiovisual Tool for Solfège Assessment

Published:16 December 2016Publication History
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Abstract

Solfège is a general technique used in the music learning process that involves the vocal performance of melodies, regarding the time and duration of musical sounds as specified in the music score, properly associated with the meter-mimicking performed by hand movement. This article presents an audiovisual approach for automatic assessment of this relevant musical study practice. The proposed system combines the gesture of meter-mimicking (video information) with the melodic transcription (audio information), where hand movement works as a metronome, controlling the time flow (tempo) of the musical piece. Thus, meter-mimicking is used to align the music score (ground truth) with the sung melody, allowing assessment even in time-dynamic scenarios. Audio analysis is applied to achieve the melodic transcription of the sung notes and the solfège performances are evaluated by a set of Bayesian classifiers that were generated from real evaluations done by experts listeners.

References

  1. Frédéric Bevilacqua, Bruno Zamborlin, Anthony Sypniewski, Norbert Schnell, Fabrice Guédy, and Nicolas Rasamimanana. 2010. Continuous realtime gesture following and recognition. In Proceedings of the 8th International Conference on Gesture in Embodied Communication and Human-Computer Interaction (GW’09). Springer-Verlag, Berlin, 73--84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Alain de Cheveigné and Hideki Kawahara. 2002. YIN, a fundamental frequency estimator for speech and music. J. Acoust. Soc. Am. 111, 4 (Apr. 2002), 1917--1930.Google ScholarGoogle Scholar
  3. Richard O. Duda, Peter E. Hart, and David G. Stork. 2001. Pattern Classification (2nd ed.). Wiley-Interscience. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Emilia Gómez and J. Bonada. 2013. Towards computer-assisted flamenco transcription: An experimental comparison of automatic transcription algorithms as applied to a Cappella singing. Comput. Music J. 37 (2013), 73--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Emile Jaques-Dalcroze. 2014. Rhythm, Music and Education. Read Books Ltd.Google ScholarGoogle Scholar
  6. Eamonn J. Keogh and Michael J. Pazzani. 2001. Derivative dynamic time warping. In Proceedings of First International Conference on Data Mining (SDM’01).Google ScholarGoogle Scholar
  7. Seong-Ju Kim. 1992. The metrically trimmed mean as a robust estimator of location. Ann. Stat. 20, 3 (Sep. 1992), 1534--1547.Google ScholarGoogle ScholarCross RefCross Ref
  8. Anssi Klapuri and Manuel Davy. 2006. Signal Processing Methods for Music Transcription. Springer-Verlag, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Maartje Koning. 2015. A New Illusion in the Perception of Relative Pitch Intervals. Ph.D. Dissertation. Faculty of Humanities of the University of Amsterdam.Google ScholarGoogle Scholar
  10. Chang-Hung Lin, Yuan-Shan Lee, Ming-Yen Chen, and Jia-Ching Wang. 2014. Automatic singing evaluating system based on acoustic features and rhythm. In Proceedings of IEEE International Conference on Orange Technologies (ICOT’14). 165--168.Google ScholarGoogle ScholarCross RefCross Ref
  11. Pieter-Jan Maes, Denis Amelynck, Micheline Lesaffre, Marc Leman, and D. K. Arvind. 2013. The “conducting master”: An interactive, real-time gesture monitoring system based on spatiotemporal motion templates. Int. J. Hum. Comput. Interact. 29, 7 (2013), 471--487.Google ScholarGoogle ScholarCross RefCross Ref
  12. Marcella Mandanici and Sylviane Sapir. 2012. Disembodied voices: A kinect virtual choir conductor. In Proceedings of the 9th Sound and Music Computing Conference, Sound and Music Computing (Eds.). 271--276.Google ScholarGoogle Scholar
  13. Matthias Mauch and Simon Dixon. 2014. pYIN: A fundamental frequency estimator using probabilistic threshold distributions. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’14). IEEE, 659--663.Google ScholarGoogle ScholarCross RefCross Ref
  14. Emilio Molina, Ana M. Barbancho, Lorenzo J. Tardón, and Isabel Barbancho. 2014. Evaluation framework for automatic singing transcription. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR’14). ISMIR, 567--572.Google ScholarGoogle Scholar
  15. Emilio Molina, Isabel Barbancho, Emilia Gómez, Ana M. Barbancho, and Lorenzo J. Tardón. 2013. Fundamental frequency alignment vs. note-based melodic similarity for singing voice assessment. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’13). 744--748.Google ScholarGoogle Scholar
  16. Emilio Molina, Lorenzo J. Tardón, Ana M. Barbancho, and Isabel Barbancho. 2015. SiPTH: Singing transcription based on hysteresis defined on the pitch-time curve. IEEE/ACM Trans. Audio, Speech Lang. Process. 23, 2 (Feb 2015), 252--263. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Meinard Müller. 2007. Information Retrieval for Music and Motion. Springer-Verlag, Berlin. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Meinard Müller. 2015. Fundamentals of Music Processing -- . Springer-Verlag, Berlin. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Eugene Narmour. 1990. The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model. The University of Chicago Press.Google ScholarGoogle Scholar
  20. Max Rudolf. 1980. The Grammar of Conducting (2nd ed.). Schirmer Books Inc., New York, NY.Google ScholarGoogle Scholar
  21. Matti Ryynänen and Anssi Klapuri. 2004. Modelling of note events for singing transcription. In Proceedings of ISCA—Tutorial and Research Workshop on Statistical and Perceptual Audio. MIT Press.Google ScholarGoogle Scholar
  22. Rodrigo Schramm, Helena de Souza Nunes, and Cláudio Rosito Jung. 2015a. Automatic Solfège assessment. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR’15). 183--189.Google ScholarGoogle Scholar
  23. Rodrigo Schramm, Cláudio Rosito Jung, and Eduardo Reck Miranda. 2015b. Dynamic time warping for music conducting gestures evaluation. IEEE Trans. Multimed. 17, 2 (Feb 2015), 243--255.Google ScholarGoogle ScholarCross RefCross Ref
  24. Keith Swanwick. 1994. Musical Knowledge, Intuition, Analysis and Music Education. Routledge, Londres.Google ScholarGoogle Scholar
  25. Robert F. Tate. 1954. Correlation between a discrete and a continuous variable. Point-biserial correlation. Ann. Math. Stat. 25, 3 (1954), 603--607.Google ScholarGoogle ScholarCross RefCross Ref
  26. Leng-Wee Toh, W. Chao, and Yi-Shin Chen. 2013. An interactive conducting system using kinect. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME). 1--6.Google ScholarGoogle Scholar
  27. Timo Viitaniemi, Anssi Klapuri, and Antti Eronen. 2003. A probabilistic model for the transcription of single-voice melodies. In Proceedings of the 2003 Finnish Signal Processing Symposium. 59--63.Google ScholarGoogle Scholar
  28. Andrew R. Webb. 2011. Statistical Pattern Recognition (3rd ed.). Wiley, Chichester, UK.Google ScholarGoogle Scholar
  29. Yang Zhang and T. F. Edgar. 2008. A robust dynamic time warping algorithm for batch trajectory synchronization. In Proceedings of American Control Conference. 2864--2869.Google ScholarGoogle Scholar
  30. Katie Zhukov. 2015. Challenging approaches to assessment of instrumental learning. In Assessment in Music Education: From Policy to Practice, Don Lebler, Gemmal Carey, and Scott D. Harrison (Eds.). Vol. 16. Springer International, Switzerland.Google ScholarGoogle Scholar

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