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Soft set theory for automatic classification of traditional pakistani musical instruments sounds

机译:传统巴基斯坦乐器声音自动分类的软集合理论

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摘要

Musical instrument classification has a great importance in data mining and multimedia. The number of studies investigating classification of musical instruments using sophisticated modeling such as neural networks, support vector machines, decision tress and rough set. However, the viability of soft set theory for musical instruments classification has not been widely experimented. Thus, this paper introduces a classification system which uses notation of soft set theory incorporating non-western musical instruments i.e. Traditional Pakistani Musical Instruments. One of the factors that may contribute to this phenomenon which are audio length, frame size and starting point of files have been investigated that might affect the performance of the classification algorithm. The modeling process comprises of three steps which are data-preprocessing, dataset partitioning and classification. Experimental results show that 94.26% was obtained from the generated datasets. Soft set theory provides fruitful investigation for musical instruments classification. These results have further expanded the scope of soft set theory for decision making applications.
机译:乐器分类在数据挖掘和多媒体中非常重要。使用复杂模型(例如神经网络,支持向量机,决策树和粗糙集)研究乐器分类的研究数量很多。但是,软集合理论在乐器分类中的可行性尚未得到广泛的试验。因此,本文介绍了一种分类系统,该系统使用软集合理论的符号并结合了非西方乐器,即传统的巴基斯坦乐器。已经研究了可能导致这种现象的因素之一,例如音频长度,帧大小和文件的起点,这些因素可能会影响分类算法的性能。建模过程包括三个步骤,即数据预处理,数据集分区和分类。实验结果表明,从生成的数据集中获得了94.26%。软集合理论为乐器的分类研究提供了丰硕的成果。这些结果进一步扩大了软集合理论在决策应用中的范围。

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