Understanding music with AI
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Experiments on the zero frequency problem
DCC '95 Proceedings of the Conference on Data Compression
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Automatic transcription of tabla music
Automatic transcription of tabla music
Methods for combining statistical models of music
CMMR'04 Proceedings of the Second international conference on Computer Music Modeling and Retrieval
"The way it Sounds": timbre models for analysis and retrieval of music signals
IEEE Transactions on Multimedia
| Hi-index | 0.00 |
We describe a realtime tabla generation system based on a variable-length n-gram model trained on a large symbolic tabla database. A novel, parametric smoothing algorithm based on a family of exponential curves is introduced to control the relative weight of high- and low-order models. This technique is shown to lead to improvements over a back-off smoothing for our tabla database. We find that cross-entropy is lowest when the coefficient of the exponential curve is between 1 and 2 and increases for values outside of this optimal range. The basic n-gram model is extended to model dependencies between duration, stroke-type, and meter using cross-products in a Multiple Viewpoints (MV) framework, leading to improvements in most cases when compared with independent stroke and duration models.