论文标题
优化音乐情感识别方法的特征选择方法
Feature Selection Approaches for Optimising Music Emotion Recognition Methods
论文作者
论文摘要
高功能维度是音乐情感识别的挑战。在音频功能和情感之间的关系上没有共识。 MER系统使用所有可用功能来识别情感。但是，这不是一个最佳解决方案，因为它包含无关的数据作为噪声。在本文中，我们介绍了一种功能选择方法，以消除MER的冗余功能。我们基于特征选择算法（FSA）创建了一个选定的功能集（SFS），并通过使用两种型号，支持向量回归（SVR）和随机森林（RF）（RF）对其进行了基准测试，并将它们与使用完整功能集（CFS）进行比较。结果表明，使用SFS的随机森林（RF）和支持向量回归（SVR）模型的MER性能都得到了改善。我们发现使用FSA可以在所有情况下提高性能，并且它具有MER任务的模型效率和稳定性的潜在好处。
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.
