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A Novel Method Based on OMPGW Method for Feature Extraction in Automatic Music Mood Classification

机译：基于OMPGW方法的音乐情绪自动分类特征提取新方法。

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摘要

Music mood is useful for music-related applications such as music retrieval or recommendation, which represents the inherent emotional expression of music signals. In this paper, a novel technique is proposed for music signal analysis in the view of emotions, which is based on the orthogonal matching pursuit, Gabor functions, and the Wigner distribution function. The technique, called the OMPGW method, consists of three-level schemes: the low-level, the middle-level and the high-level schemes. For the low-level schemes, the orthogonal matching pursuit combined with Gabor functions is proposed to provide an adaptive time-frequency decomposition of music signals. Compared with other algorithms for signal analysis, the proposed algorithm can achieve a higher spatial and temporal resolution and give a better interpret of the music signal structures. In the middle-level schemes, the Wigner distribution function is applied to obtain the time-frequency energy distribution of the results from the low-level schemes. High-level schemes are used to describe the modeling of audio features, the procedure of music mood classification. A classifier based on support vector machines is utilized to model the extracted features with the proposed technique regarding the emotion models. Several experiments are conducted with four datasets, and better results are achieved with the proposed method. In music mood classification experiments, music clips are classified into different kinds of mood clusters, and mean accuracy of 69.53 percent on our dataset can be achieved using the OMPGW method.
机译：音乐心情对于与音乐相关的应用（例如音乐检索或推荐）很有用，它表示音乐信号固有的情感表达。本文提出了一种基于情感匹配分析，Gabor函数和Wigner分布函数的基于情感的音乐信号分析新技术。该技术称为OMPGW方法，由三级方案组成：低级，中级和高级方案。对于低级方案，提出了结合Gabor函数的正交匹配追踪，以提供音乐信号的自适应时频分解。与其他信号分析算法相比，该算法可以实现更高的时空分辨率，并能更好地解释音乐信号的结构。在中级方案中，使用维格纳分布函数从低级方案获得结果的时频能量分布。高级方案用于描述音频特征的建模，音乐心情分类的过程。利用基于支持向量机的分类器，利用所提出的关于情感模型的技术对提取的特征进行建模。使用四个数据集进行了几次实验，并通过提出的方法获得了更好的结果。在音乐情绪分类实验中，音乐片段被分为不同种类的情绪簇，使用OMPGW方法可以将数据集中的平均准确率达到69.53％。

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