使用支持向量机、mlp和AdaBoost进行乐器识别,并进行形式概念分析

Swati D. Patil, P. S. Sanjekar
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引用次数: 3

摘要

乐器是由各种各样的领域组成的,因此对这些乐器进行人工分类是一项困难而具有挑战性的任务。为了使乐器分类过程简单易行,减少对人工监督的依赖,设计了给定的系统。有一些算法可以用于分类任务,我们使用SVM, MLP和AdaBoost来获得更好的结果。本系统主要采用SVM、MLP和AdaBoost分类器对乐器进行自动分类。形式概念分析技术也应用于乐器及其属性之间的关系。用支持向量机、MLP和AdaBoost分类器对该系统进行了评价,结果表明AdaBoost分类器的分类效果优于支持向量机和MLP分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Musical instrument identification using SVM, MLP& AdaBoost with formal concept analysis
Musical instruments are consist of wide variety of domain so manual classification of these instruments is difficult and challenging task. To make the process of classifying musical instrument easy and less dependent on human supervision given system is designed. There are some algorithm are available for classification tsk from which we uses SVM, MLP and AdaBoost for better result. This system mainly designed for automatic classification of musical instrument using SVM, MLP and AdaBoost classifiers. Formal Concept Analysis technique is also applied to show relationship between musical instruments and their attributes. This system is evaluated with SVM, MLP and AdaBoost classifiers which show that AdaBoost gives better result than SVM and MLP.
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