In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project
暂无分享,去创建一个
[1] Neil P. McAngus Todd,et al. Towards a cognitive theory of expression: The performance and perception of rubato , 1989 .
[2] Caroline Palmer,et al. Timing in skilled piano performance , 1989 .
[3] Hugo Fastl,et al. Psychoacoustics: Facts and Models , 1990 .
[4] Lawrence Hunter,et al. Artificial Intelligence and Molecular Biology , 1992, AI Mag..
[5] Stephen Muggleton,et al. Protein secondary structure prediction using logic-based machine learning , 1992 .
[6] N. Todd. The dynamics of dynamics: A model of musical expression , 1992 .
[7] Jude Shavlik,et al. Using neural networks to refine existing biological knowledge , 1992 .
[8] Richard A. Lewis,et al. Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[9] B. Repp. Diversity and commonality in music performance: an analysis of timing microstructure in Schumann's "Träumerei". , 1992, The Journal of the Acoustical Society of America.
[10] Johan Sundberg,et al. How can music be expressive? , 1993, Speech Commun..
[11] Raúl E. Valdés-Pérez,et al. Machine Discovery in Chemistry: New Results , 1995, Artif. Intell..
[12] Anders Friberg,et al. A Quantitative Rule System for Musical Performance , 1995 .
[13] Stanley F. Chen,et al. Bayesian Grammar Induction for Language Modeling , 1995, ACL.
[14] C. S. Wallace,et al. Unsupervised Learning Using MML , 1996, ICML.
[15] Raúl E. Valdés-Pérez,et al. A New Theorem in Particle Physics Enabled by Machine Discovery , 1996, Artif. Intell..
[16] Ian H. Witten,et al. Identifying Hierarchical Structure in Sequences: A linear-time algorithm , 1997, J. Artif. Intell. Res..
[17] W. L. Windsor,et al. Expressive Timing and Dynamics in Real and Artificial Musical Performances: Using an Algorithm as an Analytical Tool , 1997 .
[18] Ramakrishnan Srikant,et al. Fast algorithms for mining association rules , 1998, VLDB 1998.
[19] B. Repp. A microcosm of musical expression. I. Quantitative analysis of pianists' timing in the initial measures of Chopin's Etude in E major. , 1998, The Journal of the Acoustical Society of America.
[20] Raúl E. Valdés-Pérez,et al. Principles of Human Computer Collaboration for Knowledge Discovery in Science , 1999, Artif. Intell..
[21] A. Gabrielsson. The Performance of Music , 1999 .
[22] Thomas G. Dietterich. Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.
[23] Simon Dixon,et al. Beat Tracking with Musical Knowledge , 2000, ECAI.
[24] Simon Dixon. An Empirical Comparison of Tempo Trackers , 2001 .
[25] Gerhard Widmer,et al. Using AI and machine learning to study expressive music performance: project survey and first report , 2001, AI Commun..
[26] Simon Dixon,et al. Automatic Extraction of Tempo and Beat From Expressive Performances , 2001 .
[27] Gerhard Widmer,et al. Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy , 2001, ECML.
[28] Gerhard Widmer,et al. The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery , 2001, PKDD.
[29] Werner Goebl,et al. Representing expressive performance in tempo-loudness space , 2002 .
[30] Gerhard Widmer,et al. Towards a Simple Clustering Criterion Based on Minimum Length Encoding , 2002, ECML.
[31] Elias Pampalk,et al. Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps , 2002, ICANN.
[32] Gerhard Widmer,et al. Machine Discoveries: A Few Simple, Robust Local Expression Principles , 2002 .
[33] Gerhard Widmer,et al. The Performance Worm: Real Time Visualisation of Expression based on Langner's Tempo-Loudness Animation , 2002, ICMC.
[34] Werner Goebl,et al. Visualizing Expressive Performance in Tempo—Loudness Space , 2003, Computer Music Journal.
[35] Gerhard Widmer,et al. Playing Mozart by Analogy: Learning Multi-level Timing and Dynamics Strategies , 2003 .
[36] Heikki Mannila,et al. Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.