Music Emotion Detection Using Hierarchical Sparse Kernel Machines

Abstract

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

Music emotion recognition using PSO‐based fuzzy hyper‐rectangular composite neural networks

“…This paper adopts a standard dataset that has been used in several works [9]. The dataset collected eighty songs from two websites to construct an emotional music database.…”

“…In previous work, it has been proved that the emotional content in music can been retrieved by extracting raw features [5, 9, 16]. There are 15 kinds of feature extracted from music clips in this paper.…”

“…In [16], features are mapped into emotion categories on emotion plane, then two support vector regression are trained to predict the arousal and valence value. In our previous work [9], we used a hierarchical framework to detect emotion from acoustic music data. In [5], support vector machine (SVM) is adopted to detect emotional content in music.…”

“…The emotional content of the music can be represented by two ways, i.e. an emotional plane [7] and labels [8, 9]. In the emotional plane, the emotional state can be quantised, and then it can be considered as a pair of valence and arousal value (VA value).…”