|
32
|
AI and Music: From composition to expressive performance
– Lopez de Mantaras, R Arcos, J L
- 2002
|
|
49
|
The dynamics of dynamics: A model of musical expression
– N P McAngus Todd
- 2011
|
|
22
|
Machine Discoveries: A Few Simple, Robust Local Expression Principles
– Gerhard Widmer
- 2001
|
|
46
|
Diversity and commonality in music performance - An analysis of timing microstructure in Schumann’s “Träumerei
– Bruno H Repp
- 1992
|
|
962
|
Learning Stochastic Logic Programs
– Stephen Muggleton
- 2000
|
|
20
|
Using AI and Machine Learning to Study Expressive Music Performance: project Survey and First Report
– Gerhard Widmer
- 2001
|
|
11
|
The Performance Worm: Real Time Visualisation of Expression based on Langner's Tempo-Loudness Animation
– Simon Dixon, Werner Goebl, Gerhard Widmer
- 2002
|
|
15
|
Music Performance
– A Gabrielsson
- 1999
|
|
121
|
Automatic Extraction of Tempo and Beat from Expressive Performances
– Simon Dixon
- 2001
|
|
9
|
Exploring expressive performance trajectories: Six famous pianists play six Chopin pieces
– W Goebl, E Pampalk, G Widmer
- 2004
|
|
21
|
Computational models of expressive music performance: The state of the art
– Gerhard Widmer, Werner Goebl
- 2004
|
|
4
|
Automatic Recognition of Famous Artists by Machine
– Gerhard Widmer, Patrick Zanon
- 2004
|
|
15
|
An Interactive Beat Tracking and Visualisation System
– Simon Dixon
- 2001
|
|
32
|
Rhythm and Timing in Music
– E Clarke
- 1999
|
|
21
|
Using string kernels to identify famous performers from their playing style
– Craig Saunders, David R. Hardoon, John Shawe-taylor, Gerhard Widmer
- 2004
|
|
18
|
Visualizing Expressive Performance In Tempo-Loudness Space
– Jörg Langner, Werner Goebl
- 2003
|
|
131
|
Identifying hierarchical structure in sequences: A linear-time algorithm
– Craig G. Nevill-manning, Ian H. Witten
- 1997
|
|
173
|
Rijsbergen, Information Retrieval
– C J van
- 1979
|
|
187
|
Psychoacoustics. Facts and Models
– H Fastl, E Zwicker
- 2007
|