A Machine Learning Toolbox For Musician Computer Interaction

A Machine Learning Toolbox For Musician Computer Interaction
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用于音乐家计算机交互的机器学习工具箱

DOI：
10.5281/zenodo.1178031
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发表时间：
2011
期刊：
--
影响因子：
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通讯作者：
Sile O'Modhrain
Sile O'Modhrain
中科院分区：
--
文献类型：
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作者：
N. Gillian;R. B. Knapp;Sile O'Modhrain

文献摘要

被引文献

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本文介绍了SARC EyesWeb Catalog（SEC），这是一个专门为音乐家-计算机交互开发的机器学习工具箱。SEC具有大量的机器学习算法，可用于实时识别静态姿势，执行回归和对多变量时间姿势进行分类。工具箱中的算法被设计用于处理任何N维信号，并且可以使用少量的训练示例进行快速训练。我们还提供了用于识别音乐手势的算法的动机，以实现较低的个人内概括误差，而不是在人机交互的其他领域更常见的个人间概括误差。
This paper presents the SARC EyesWeb Catalog, (SEC), a machine learning toolbox that has been specically developed for musician-computer interaction. The SEC features a large number of machine learning algorithms that can be used in real-time to recognise static postures, perform regression and classify multivariate temporal gestures. The algorithms within the toolbox have been designed to work with any N-dimensional signal and can be quickly trained with a small number of training examples. We also provide the motivation for the algorithms used for the recognition of musical gestures to achieve a low intra-personal generalisation error, as opposed to the inter-personal generalisation error that is more common in other areas of humancomputer interaction.