A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments

References

• Arzt, A., Widmer, G., & Dixon, S. (2012). Adaptive distance normalization for real-time music tracking. Proceedings of EUSIPCO (pp. 2689–2693). Piscataway, NJ: IEEE.
   Google Scholar
• Bloch, J., & Dannenberg, R. (1985). Real-time computer accompaniment of keyboard performances. Proceedings of ICMC (pp. 279–290). Ann Arbor, MI: Michigan Publishing.
   Google Scholar
• Boulez, P. (1963). Penser la musique aujourd’hui. Paris: Gouthier.
   Google Scholar
• Cano, P., Loscos, A., & Bonada, J. (1999). Score-performance matching using HMMs. Proceedings of ICMC (pp. 441–444). Ann Arbor, MI: Michigan Publishing.
   Google Scholar
• Cemgil, A. T., Desain, P., & Kappen, B. (2000). Rhythm quantization for transcription. Computer Music Journal, 24(2), 60–76.
   Web of Science ®Google Scholar
• Cemgil, A. T., & Kappen, B. (2003). Monte Carlo methods for tempo tracking and rhythm quantization. Journal of Artificial Intelligence Research, 18(1), 45–81.
   Google Scholar
• Cemgil, A. T., Kappen, B., Desain, P., & Honing, H. (2000). On tempo tracking: Tempogram representation and Kalman filtering. Journal of New Music Research, 29(4), 259–273.
   Web of Science ®Google Scholar
• Chopin, F. (1879). Études Op. 10 and Op. 25. Scholtz, H. ed. Sämtliche Pianoforte-Werke. C.F. Peters.
   Google Scholar
• Conklin, D. (2002). Representation and discovery of vertical patterns in music. Anagnostopoulou, C., Ferrand, M., & Smaill, A. eds. Music and artificial intelligence. Berlin: Springer. (pp. 32–42). Lecture Notes in Artificial Intelligence 2445.
   Google Scholar
• Cont, A. (2010). A coupled duration-focused architecture for realtime music to score alignment. IEEE Transactions on PAMI, 32(6), 974–987.
   Web of Science ®Google Scholar
• Dannenberg, R. (1984). An on-line algorithm for real-time accompaniment. Proceedings of ICMC (pp. 193–198). Ann Arbor, MI: Michigan Publishing.
   Google Scholar
• Dannenberg, R., & Mukaino, H. (1988). New techniques for enhanced quality of computer accompaniment. Proceedings of ICMC (pp. 243–249). Ann Arbor, MI: Michigan Publishing.
   Google Scholar
• Desain, P., & Honing, H. (1994). Does expressive timing in music performance scale proportionally with tempo? Psychological Research, 56, 285–292.
   Web of Science ®Google Scholar
• Desain, P., Honing, H., & Heijink, H. (1997). Robust score-performance matching: Taking advantage of structural information. Proceedings of ICMC. San Francisco, CA: ICMA. (pp. 337–340).
   Google Scholar
• Duan, Z., & Pardo, B. (2011). A state space model for online polyphonic audio-score alignment. Proceedings of ICASSP (pp. 197–200). Piscataway, NJ: IEEE.
   Google Scholar
• Ferguson, J. (1980). Variable duration models for speech. Proceedings of the Symposium on the Applications of Hidden Markov Models to Text and Speech (pp. 143–179). Princeton, NJ.
   Google Scholar
• Fine, S., Singer, Y., & Tishby, N. (1998). The hierarchical hidden markov model: Analysis and applications. Machine Learning, 32(1), 41–62.
   Web of Science ®Google Scholar
• Gingras, B., & McAdams, S. (2011). Improved score-performance matching using both structural and temporal information from MIDI recordings. Journal of New Music Research, 40(1), 43–57.
   Web of Science ®Google Scholar
• Grindlay, G., & Helmbold, D. (2006). Modeling, analyzing, and synthesizing expressive piano performance with graphical models. Machine Learning, 65(2–3), 361–387.
   Web of Science ®Google Scholar
• Hashida, M., Matsui, T., & Katayose, H. (2008). A new music database describing deviation information of performance expressions. Proceedings of ISMIR. Philadelphia: Drexel University. (pp. 489–494).
   Google Scholar
• Heijink, H., Windsor, L., & Desain, P. (2000). Data processing in music performance research: Using structural information to improve score-performance matching. Behavior Research Methods, Instruments, & Computers, 32(4), 546–554.
   PubMedGoogle Scholar
• Kim, C.-J. (1994). Dynamic linear models with Markov-switching. Journal of Econometrics, 60, 1–22.
   Web of Science ®Google Scholar
• Large, E., & Jones, M. (1999). Dynamics of attending: How people track time-varying events. Psychological Review, 106(1), 119–159.
   Web of Science ®Google Scholar
• Montecchio, N., & Cont, A. (2011). A unified approach to real time audio-to-score and audio-to-audio alignment using sequential Montecarlo inference techniques. Proceedings of ICASSP (pp. 193–196). IEEE: Piscataway, NJ.
   Google Scholar
• Nakamura, E., Nakamura, T., Saito, Y., Ono, N., & Sagayama, S. (2014). Outer-product hidden Markov model and polyphonic MIDI score following. Journal of New Music Research, 43(2), 183–201.
   Web of Science ®Google Scholar
• Neumann, F. (1983). Ornamentation in Baroque and post-Baroque music. Princeton University Press.
   Google Scholar
• Orio, N., Lemouton, S. & Schwarz, D. (2003). Score following: State of the art and new developments. Proceedings of new interfaces for musical expression (pp. 36–41). Montréal: Faculty of Music, McGill University.
   Google Scholar
• Otsuka, T., Nakadai, K., Takahashi, T., Ogata, T., & Okuno, H. (2011). Real-time audio-to-score alignment using particle filter for coplayer music robots. EURASIP Journal on Advances in Signal Processing, 2011, 384651.
   Web of Science ®Google Scholar
• Otsuki, T., Saitou, N., Nakai, M., Shimodaira, H., & Sagayama, S. (2002). Musical rhythm recognition using hidden Markov model. Journal of Information Processing Society of Japan, 43(2), 245–255.
   Google Scholar
• Palmer, C. (1997). Music performance. Annual Review of Psychology, 48, 115–138.
   PubMed Web of Science ®Google Scholar
• Pardo, B., & Birmingham, W. (2005). Modeling form for on-line following of musical performances. Proceedings of the Twentieth National Conference on Artificial Intelligence. Palo Alto, CA: AAAI.
   Google Scholar
• Raphael, C. (1999). Automatic segmentation of acoustic musical signals using hidden Markov models. IEEE Transactions on PAMI, 21(4), 360–370.
   Web of Science ®Google Scholar
• Raphael, C. (2001). Automatic segmentation of acoustic musical signals using hidden Markov models. Journal of Computational and Graphical Statistics, 10(3), 487–512.
   Web of Science ®Google Scholar
• Read, G. (1969). Music notation: A manual of modern practice. (2nd ed.) Allyn and Bacon.
   Google Scholar
• Repp, B. (1997). Some observations on pianists’ timing of arpeggiated chords. Psychology of Music, 25, 133–148.
   Google Scholar
• Sadie, S. (Ed.). (2001). The new Grove dictionary of music and musicians (2nd ed., Oxford Music Online). Oxford: Oxford University Press.
   Google Scholar
• Saito, N., Nakai, M., Shimodaira, H., & Sagayama, S. (1999). Hidden Markov model for restoration of musical note sequence from the performance (in Japanese). (Technical Report of IPSJ Special Interest Group on Music and Computer (pp. 27–32). Tokyo: Information Processing Society of Japan.
   Google Scholar
• Schwarz, D., Orio, N. & Schnell, N. (2004). Robust polyphonic MIDI score following with hidden Markov models. Proceedings of ICMC (pp. 442–445). Ann Arbor, MI: Michigan Publishing.
   Google Scholar
• Tekin, M. (2006). Intelligent multi-agent systems in polyphonic score following (PhD thesis). Queen’s University Belfast, Northern Ireland.
   Google Scholar
• T{\"u}rk, D.G. (1789). Klavierschule, oder Anweisung zum Klavierspielen für Lehrer und Lernende, mit kritischen Anmerkungen. Leipzig & Halle.
   Google Scholar
• Vercoe, B. (1984). The synthetic performer in the context of live performance. Proceedings of ICMC (pp. 199–200). Ann Arbor, MI: Michigan Publishing.
   Google Scholar
• Windsor, L., Aarts, R., Desain, P., Heijink, H., & Timmers, R. (2001). The timing of grace notes in skilled musical performance at different tempi: a preliminary case study. Psychology of Music, 29, 149–169.
   Google Scholar