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.
- 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 Scholar
Digital Library
- 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 Scholar
- Richard O. Duda, Peter E. Hart, and David G. Stork. 2001. Pattern Classification (2nd ed.). Wiley-Interscience. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- Emile Jaques-Dalcroze. 2014. Rhythm, Music and Education. Read Books Ltd.Google Scholar
- Eamonn J. Keogh and Michael J. Pazzani. 2001. Derivative dynamic time warping. In Proceedings of First International Conference on Data Mining (SDM’01).Google Scholar
- Seong-Ju Kim. 1992. The metrically trimmed mean as a robust estimator of location. Ann. Stat. 20, 3 (Sep. 1992), 1534--1547.Google Scholar
Cross Ref
- Anssi Klapuri and Manuel Davy. 2006. Signal Processing Methods for Music Transcription. Springer-Verlag, New York, NY. Google Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
- 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 Scholar
Digital Library
- Meinard Müller. 2007. Information Retrieval for Music and Motion. Springer-Verlag, Berlin. Google Scholar
Digital Library
- Meinard Müller. 2015. Fundamentals of Music Processing -- . Springer-Verlag, Berlin. Google Scholar
Digital Library
- Eugene Narmour. 1990. The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model. The University of Chicago Press.Google Scholar
- Max Rudolf. 1980. The Grammar of Conducting (2nd ed.). Schirmer Books Inc., New York, NY.Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
Cross Ref
- Keith Swanwick. 1994. Musical Knowledge, Intuition, Analysis and Music Education. Routledge, Londres.Google Scholar
- Robert F. Tate. 1954. Correlation between a discrete and a continuous variable. Point-biserial correlation. Ann. Math. Stat. 25, 3 (1954), 603--607.Google Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
- Andrew R. Webb. 2011. Statistical Pattern Recognition (3rd ed.). Wiley, Chichester, UK.Google Scholar
- 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 Scholar
- 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 Scholar
Index Terms
Audiovisual Tool for Solfège Assessment
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