基于NCC和k-NN的传统与现代音乐分类

Elizabeth Nurmiyati Tamatjita, Aditya W. Mahastama
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引用次数: 0

摘要

当前位置音乐是人与人之间互动的一种方式,通过声音感觉来传递情感。音乐由合奏的乐器组成,偶尔与声乐一起演奏,以形成和谐。某些乐器的存在可以用来确定音乐的类型,进而确定它的起源。这项研究从印尼的角度,根据乐器和节拍,对传统音乐、当地当代音乐和外国音乐进行了分类。在本研究中选择代表音乐的流派分为六类:巴厘岛,爪哇,巽他(传统),Keroncong(当地当代),古典和拉丁(外国)。180首乐曲用于训练,同样数量的乐曲用于测试;使用样本的作品只有乐器和乐器与声乐。为了提取其特征,将每个音乐片段切割成30ms的切片,然后从每个片段中提取3个时域特征的代表性向量。然后使用k=3和k=5的最近邻分类器(NCC)和k-近邻(k- nn)对测试数据进行分类。使用k=3的k- nn获得了最好的结果,对巴厘语产生了96.6%的最高准确率,所有类型的平均值为73.89%。准确率最低的是古典类,在三次测试中,准确率一直在50%以下,平均为36.67%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Traditional and Modern Music using NCC and k-NN
: Music is a means of interaction between humans which is transmitted as a presentation of feelings through acoustic sensation. Music consists of instruments played ensemble, occasionally with vocal, to form a harmony. The presence of certain instruments can be used to identify the genre of a music, and in turn its origin. This research conducted classification of traditional, local contemporary, and foreign music – from Indonesian point of view – according to instruments and beats. Genres chosen to represent the music in this research, fall into six categories: Balinese, Javanese, Sundanese (traditional), Keroncong (local contemporary), Classical and Latin (foreign). 180 pieces of music are used for training, and the same number of pieces are used for testing; using samples of pieces with instruments only and also instruments with vocal. To extract its features, each music pieces are cut into 30ms slices, then a representative vector of 3 time-domain features is taken from every piece. Classification of test data is then conducted using Nearest Centroid Classifier (NCC) and k-Nearest Neighbour (k-NN) with k=3 and k=5. Best results are obtained using k-NN with k=3, generating the maximum 96.6% accuracy for Balinese, with an all-genre average of 73.89%. The lowest accuracy rate belongs to Classical category, in which from the three tests, it is consistently rated under 50% with average of 36.67%.
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