Music recognition system using ART-1 and GA

Sang Moon Soak; Seok Cheol Chang; Taehwan Shin; Byung-Ha Ahn

doi:10.1117/12.458413

6 March 2002 Music recognition system using ART-1 and GA

Sang Moon Soak, Seok Cheol Chang, Taehwan Shin, Byung-Ha Ahn

Proceedings Volume 4734, Optical Pattern Recognition XIII; (2002) https://doi.org/10.1117/12.458413
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Previously, most optical music recognition (OMR) systems have used the neural network, and used mainly back- propagation training method. One of the disadvantages of BP is that much time is required to train data sets. For example, when new data sets are added, all data sets have to be trained. Another disadvantage is that weighting values cannot be guaranteed as global optima after training them. It means that weighting values can fall down to local optimum solution. In this paper, we propose the new OMR method which combines the adaptive resonance theory (ART-1) with the genetic algorithms (GA). For reducing the training time, we use ART-1 which classifies several music symbols. It has another advantage to reduce the number of datasets, because classified symbols through ART-1 are used as input vectors of BP. And for guaranteeing the global optima in training data set, we use GA which is known as one of the best method for finding optimal solutions at complex problems.

Citation Download Citation

Sang Moon Soak, Seok Cheol Chang, Taehwan Shin, and Byung-Ha Ahn "Music recognition system using ART-1 and GA", Proc. SPIE 4734, Optical Pattern Recognition XIII, (6 March 2002); https://doi.org/10.1117/12.458413

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available