Music score alignment and computer accompaniment

ABSTRACT

By relating musical sound to musical notation, these systems generate tireless, expressive musical accompaniment to follow and sometimes learn from a live human performance.

REFERENCES

• 1: Baird, B., Blevins, D., and Zahler, N. Artificial intelligence and music: Implementing an interactive computer performer. Computer Music Journal 17, 2 (Summer 1993), 73--79.
• 2: Dannenberg, R., Sanchez, M., Joseph, A., Capell, P., Joseph, R., and Saul, R. A computer-based multimedia tutor for beginning piano students. Interface Journal of New Music Research 19, 2--3 (1993), 155--173.
• 3: Roger Dannenberg, Real-time scheduling and computer accompaniment, Current directions in computer music research, MIT Press, Cambridge, MA, 1989
• 4: Grubb, L. and Dannenberg, R. A stochastic method of tracking a vocal performer. In Proceedings of the 1997 International Computer Music Conference (Thessaloniki, Greece, Sept. 25--30). International Computer Music Association, San Francisco, 1997, 301--308.
• 5: Hu, N., Dannenberg, R., and Tzanetakis, G. Polyphonic audio matching and alignment for music retrieval. In Proceedings of the 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (New Paltz, NY, Oct. 19--22). IEEE Computer Society Press, Piscataway, NJ, 2003, 185--188.
• 6: Loscos, A., Cano, P., and Bonada, J. Score-performance matching using hidden Markov models. In Proceedings of the 1999 International Computer Music Conference (Beijing, Oct. 22--27). International Computer Music Association, San Francisco, 1999, 441--444.
• 7: Orio, N. and Schwarz, D. Alignment of monophonic and polyphonic music to a score. In Proceedings of the 2001 International Computer Music Conference (Havana, Sept. 17--22). International Computer Music Association, San Francisco, 2001, 155--158.
• 8: Raphael, C. A hybrid graphical model for aligning polyphonic audio with musical scores. In Proceedings of the Fifth International Conference on Music Information Retrieval (Barcelona, Spain, Oct. 10--14). Audiovisual Institute Pompeu Fabra University, Barcelona, Spain, 2004, 387--394.
• 9: Raphael, C. A Bayesian network for real-time musical accompaniment. In Proceedings of Neural Information Processing Systems (NIPS) 14 (Vancouver, B.C., Dec. 3--8). MIT Press, Cambridge, MA, 2001.
• 10: Christopher Raphael, Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.21 n.4, p.360-370, April 1999 [doi>10.1109/34.761266 ]
• 11: Schwarz, D., Cont, A., and Schnell, N. From Boulez to ballads: Training IRCAM's score follower. In Proceedings of the 2005 International Computer Music Conference (Barcelona, Spain, Sept. 5--9). International Computer Music Association, San Francisco, 2005.
• 12: Vercoe, B. The synthetic performer in the context of live performance. In Proceedings of the International Computer Music Conference 1984 (Paris, Oct. 19--23, 1984). International Computer Music Association, San Francisco, 1985, 199--200.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.5 Sound and Music Computing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.0 General
      H.3.1 Content Analysis and Indexing
      H.3.2 Information Storage
      H.3.3 Information Search and Retrieval
      H.3.4 Systems and Software
      H.3.5 On-line Information Services
      H.3.6 Library Automation
      H.3.7 Digital Libraries
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

  J. Computer Applications
  J.5 ARTS AND HUMANITIES

  K. Computing Milieux
  K.4 COMPUTERS AND SOCIETY
      K.4.4 Electronic Commerce
  K.5 LEGAL ASPECTS OF COMPUTING
      K.5.0 General
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.5 Security and Protection (D.4.6, K.4.2)