Sketchpad a man-machine graphical communication system
25 years of DAC Papers on Twenty-five years of electronic design automation
Proceedings of the 11th annual ACM symposium on User interface software and technology
Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design of a Pen-Based Musical Input System
OZCHI '96 Proceedings of the 6th Australian Conference on Computer-Human Interaction (OZCHI '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Fast HMM Algorithm Based on Stroke Lengths for On-Line Recognition of Handwritten Music Scores
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
An online handwritten music symbol recognition system
International Journal on Document Analysis and Recognition
Online Pen-Based Recognition of Music Notation with Artificial Neural Networks
Computer Music Journal
| Hi-index | 0.00 |
A Hidden Markov Model (HMM) is a powerful model in describing temporal sequences. The HMM parameters are usually estimated using Baum-Welch algorithm. However, it is well known that the Baum-Welch algorithm tends to arrive at local optimal points. In this report, we investigate the potential of the Particle Swarm Optimisation (PSO) as an alternative method for HMM parameters estimation. The domain in this study is the recognition of handwritten music notations. Three observables: (i) sequence of ink patterns, (ii) stroke information and (iii) spatial information associated with eight musical symbols were recorded. Sixteen HMM models were built from the data. Eight HMM models for eight musical symbols were built from the parameters estimated using the Baum-Welch algorithm and the other eight models were built from the parameters estimated using PSO. The experiment shows that the performances of HMM models, using parameters estimated from PSO and Baum-Welch approach, are comparable. We suggest that PSO or a combination of PSO and Baum-Welch algorithm could be alternative approaches for the HMM parameters estimation.