The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task.
翻译:高特征维度是音乐情感识别的一个挑战。 在音频特征和情感之间的关系上没有共识。 市面汇率系统使用所有可用的特征来识别情感; 然而,这不是一个最佳解决方案,因为它包含不相关的数据作为噪音。 在本文中,我们引入了一种特征选择方法来消除市面汇率的冗余特征。 我们根据特征选择算法(FSA)创建了一个部分特征集(SFS),并通过两个模型(支持矢量回归和随机森林(RF))的培训对其进行基准化,并将它们与使用全特性集(CFS)进行比较。 结果表明,市面汇率的性能通过使用SFS来改善随机森林(RF)和支持矢量回归(SVR)模式。 我们发现,使用FSA可以改善所有情景的性能,并且通过使用支持矢量回归和随机森林(RF)两个模型的性能和稳定性来对MER任务的潜在好处。