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Automatic mood detection of indian music using mfccs and k-means algorithm

机译：使用mfccs和k-means算法自动检测印度音乐的情绪

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摘要

This paper proposes a method of identifying the mood underlying a piece of music by extracting suitable and robust features from music clip. To recognize the mood, K-means clustering and global thresholding was used. Three features were amalgamated to decide the mood tag of the musical piece. Mel frequency cepstral coefficients, frame energy and peak difference are the features of interest. These features were used for clustering and further achieving silhouette plot which formed the basis of deciding the limits of threshold for classification. Experiments were performed on a database of audio clips of various categories. The accuracy of the mood extracted is around 90% indicating that the proposed technique provides encouraging results.
机译：本文提出了一种通过从音乐片段中提取合适且健壮的特征来识别音乐背后情绪的方法。为了识别情绪，使用了K均值聚类和全局阈值。合并了三个功能来确定音乐作品的心情标签。梅尔频率倒谱系数，帧能量和峰差是您感兴趣的特征。这些特征被用于聚类并进一步获得轮廓图，轮廓图成为确定分类阈值极限的基础。在各种类别的音频剪辑的数据库上进行了实验。提取的情绪的准确性约为90％，表明所提出的技术提供了令人鼓舞的结果。

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