文章基本信息

标题：Improved Self-Organizing Maps and Speech Compression
本地全文：下载
作者：Mohamed Ettaouil ; Mohamed Lazaar
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：2
出版社：IJCSI Press
摘要：Recent developments of Self-Organizing Maps or Kohonen networks become more and more interesting in many fields such as: pattern recognition, clustering, speech recognition, data compression, medical diagnosis... Kohonen networks is unsupervised learning models. The results obtained by the Kohonen networks are dependent on their parameters such as the architecture of the Kohonen map, the later has a great impact on the convergence of learning methods. The selection of the architecture of Kohonen networks, associated with a given problem, is one of the most important research problems in the neural network research. In this paper, we model this problem of neural architecture in terms of a mixed-integer non linear problem with linear constraints. To solve this model of optimization for the network architectures, we propose the genetic algorithm. Also, we implemented and evaluated the proposed method and speech compression algorithms. Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. The numerical results demonstrated the effectiveness of the new model.
关键词：Kohonen networks; genetic algorithms; Speech compression; unsupervised training; mixed;integer non;linear programming; Mel Frequency Cepstral Coefficient (MFCC).