Happiness detection in music using hierarchical SVMs with dual types of kernels

In this paper, we proposed a novel system for detecting happiness emotion in music. Two emotion profiles are constructed using decision value in support vector machine (SVM), and based on short term and long term feature respectively. When using short term feature to train models, the kernel used in SVM is probability product kernel. If the input feature is long term, the kernel used in SVM is RBF kernel. SVM model is trained from a raw feature set comprising the following types of features: rhythm, timbre, and tonality. Each SVM is applied to targeted emotion class with calm emotion as the background class to train hyperplanes respectively. With the eight hyperplanes trained from angry, happy, sad, relaxed, pleased, bored, nervous, and peaceful, each test clip can output four decision values, which are then regarded as the emotion profile. Two profiles are fusioned to train SVMs. The final decision value is then extracted to draw DET curve. The experiment result shows that the proposed system has a good performance on music emotion recognition.