Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Digital violin tutor: an integrated system for beginning violin learners
Proceedings of the 13th annual ACM international conference on Multimedia
Variations2: retrieving and using music in an academic setting
Communications of the ACM - Music information retrieval
Human model evaluation in interactive supervised learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Building a personalized audio equalizer interface with transfer learning and active learning
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Learning and extraction of violin instrumental controls from audio signal
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
| Hi-index | 0.00 |
This work improves the realism of synthesis and performance of string quartet music by generating audio through physical modelling of the violins, viola, and cello. To perform music with the physical models, virtual musicians interpret the musical score and generate actions which control the physical models. The resulting audio and haptic signals are examined with support vector machines, which adjust the bowing parameters in order to establish and maintain a desirable timbre. This intelligent feedback control is trained with human input, but after the initial training is completed, the virtual musicians perform autonomously. The system can synthesize and control different instruments of the same type (e.g., multiple distinct violins) and has been tested on two distinct string quartets (total of 8 violins, 2 violas, 2 cellos). In addition to audio, the system creates a video animation of the instruments performing the sheet music.