Automatic Music Genre Classification Based on Linguistic Frequencies Using Machine Learning

M. S. Rao, O. Pavan Kalyan, N. N. Kumar, Md. Tasleem Tabassum, B. Srihari
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Abstract

Classifying various music into its genre has a lot of applications in the real world. It plays an important role in several online music streaming services such as Gaana, Spotify etc. Most of the music recommender systems implement such feature. Over the past two decades music coming from various sources has been increasing at a high speed. Several musical communities are emerged based on the music genre. Therefore, in order to satisfy their requirements, the need for an automatic music genre classifier became evident. In the process of determining the genre of a music, accuracy of the prediction must be well maintained. In our project we are automatically classifying an unknown music into its genre with an effective accuracy. We are separating the linguistic content from the noise while extracting features from the set of audio files. This helps in obtaining a good accuracy of prediction. We are implementing various Machine Learning Algorithms to build our project. We considered the GTZAN dataset [4], which contains 1000 music files of 10 different genres with each file having a duration of 30 sec.
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基于语言频率的机器学习自动音乐类型分类
将不同的音乐分类成不同的流派在现实世界中有很多应用。它在Gaana、Spotify等在线音乐流媒体服务中发挥着重要作用。大多数音乐推荐系统都实现了这样的功能。在过去的二十年里,来自各种来源的音乐一直在高速增长。根据音乐类型,出现了几个音乐团体。因此,为了满足他们的需求,对自动音乐类型分类器的需求变得明显。在确定音乐类型的过程中,必须很好地保持预测的准确性。在我们的项目中,我们以有效的准确性自动将未知的音乐分类为其类型。我们在从一组音频文件中提取特征的同时将语言内容从噪声中分离出来。这有助于获得较高的预测精度。我们正在实现各种机器学习算法来构建我们的项目。我们考虑GTZAN数据集[4],它包含1000个10种不同类型的音乐文件,每个文件的持续时间为30秒。
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