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首页> 外文会议>Telecommunications and Signal Processing (TSP), 2012 35th International Conference on >A novel supervised learning algorithm for musical instrument classification
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A novel supervised learning algorithm for musical instrument classification

机译：一种新颖的乐器分类监督学习算法

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摘要

In this paper, a novel supervised learning algorithm for automatic classification of individual musical instrument sounds is addressed deriving from the idea of supervised non-negative matrix factorization (NMF) algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which supervised NMF algorithm is unable to do. Afterwards, each data is projected onto several training orthogonal basis matrices and three classifiers have been employed to compare the performance with different methods. In addition, feature selection is also applied in order to choose the most discriminative features for instrument classification. The results indicate that the classification accuracy of proposed method is 87.6%, which is comparable to the performance of supervised NMF algorithm for the same experiments.
机译：本文从监督非负矩阵分解（NMF）算法的思想出发，提出了一种新颖的监督学习算法，用于对乐器声音进行自动分类。在我们的方法中，无需迭代更新矩阵即可获得正交基矩阵，这是有监督的NMF算法无法做到的。然后，将每个数据投影到几个训练正交基矩阵上，并使用三个分类器比较不同方法的性能。此外，还应用了特征选择功能，以便为仪器分类选择最具区分性的特征。结果表明，该方法的分类精度为87.6％，与相同实验的监督NMF算法的性能相当。

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