在Leap运动控制器上用k近邻方法匹配Kodàly手势的音调分类

Muhammad Croassacipto, M. Ichwan, Dina Budhi Utami
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引用次数: 1

摘要

手可以做出各种各样的姿势,其中每个姿势都可以有一个意义或目的,可以作为一种交流的形式,根据一个普遍的协议或谁交流。手的姿势可以作为人类与计算机交互的更快、直观、符合人体自然功能的手势。其中一个是Kodàly Handsign,由匈牙利作曲家Zoltán Kodály制作,这是匈牙利音乐教育中的一个概念。这个手势用于交互式angklung表演,以确定将由基于手部姿势的k -最近邻(KNN)算法分类过程播放的音调。这个分类过程是对从Leap Motion Controller提取的数据进行的,Leap Motion Controller根据飞机的基本原理获取Pitch、Roll和Yaw的值。研究结果在21099训练数据上以k值为周期1,3,5,7,9进行了五次测试,测试数据包括874 Do', 702 Si, 913 La, 612 Sol, 661 Fa, 526 Mi, 891 Re和1004 Do标点符号。测试结果表明,在k=1的最优k值下,该方法可以识别手部姿势,准确率达到94.87%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tone Classification Matches Kodàly Handsign with the K-Nearest Neighbor Method at Leap Motion Controller
Hands can produce a variety of poses in which each pose can have a meaning or purpose that can be used as a form of communication determined according to a general agreement or who communicate. Hand pose can be used as human interaction with the computer is faster, intuitive, and in line with the natural function of the human body called Handsign. One of them is Kodàly Handsign, made by a Hungarian composer named Zoltán Kodály, which is a concept in music education in Hungary. This hand sign is used in interactive angklung performances in determining the tone that will be played by the K-Nearest Neighbor (KNN) algorithm classification process based on hand poses. This classification process is performed on the extracted data from Leap Motion Controller, which takes Pitch, Roll, and Yaw values based on basic aircraft principle. The results of the research were conducted five times with the value of k periodically 1,3,5,7,9 with test data consisting pose of 874 Do', 702 Si, 913 La, 612 Sol, 661 Fa, 526 Mi, 891 Re, and 1004 Do punctuation on 21099 training data. The test results can recognize hand poses with the optimal k value k=1 with an accuracy level of 94.87%.
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