Much of the appeal of music lies in its power to convey emotions moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.
Recent years have witnessed growing attempts to model the emotion of a music piece as a probability distribution in the VA space @cite_51 @cite_75 @cite_63 @cite_71 to better account for the subjective nature of emotion perception. For instance, Figure shows the VA values applied by different annotators to four music pieces. To characterize the distribution of the emotion annotations for each clip, a typical way is to use a bivariate Gaussian distribution, where the mean vector presents the most possible VA values and the covariance matrix indicates its uncertainty. For a clip with highly subjective affective content, the determinant of the covariance matrix would be larger.
@cite_51 @cite_75 @cite_63 @cite_71
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Music emotion recognition is concerned with developing predictive models that comprehend the affective content of musical signals. Recently, a growing number of attempts has been made to model the music emotion as a probability distribution in the valence-arousal (VA) space to better account for the subjectivity. In this paper, we present a novel histogram density modeling approach that models the emotion distribution by a 2-D histogram over the quantized VA space and learns a set of latent histograms to predict the emotion probability density of a song from audio. The proposed model is free from parametric distribution assumptions over the VA space, easy to implement, and extremely fast to train. We also extend our model to deal with the temporal dynamics of time-varying emotion labels. Comprehensive performance study on two larger-scale datasets demonstrates that our approach achieves comparable performance to the state-of-the-art ones, but with much better training and testing efficiency. Music is composed to be emotionally expressive, and emotional associations provide an especially natural domain for indexing and recommendation in today's vast digital music libraries. But such libraries require powerful automated tools, and the development of systems for automatic prediction of musical emotion presents a myriad challenges. The perceptual nature of musical emotion necessitates the collection of data from human subjects. The interpretation of emotion varies between listeners thus each clip needs to be annotated by a distribution of subjects. In addition, the sharing of large music content libraries for the development of such systems, even for academic research, presents complicated legal issues which vary by country. This work presents a new publicly available dataset for music emotion recognition research and a baseline system. In addressing the difficulties of emotion annotation we have turned to crowdsourcing, using Amazon Mechanical Turk, and have developed a two-stage procedure for filtering out poor quality workers. The dataset consists entirely of creative commons music from the Free Music Archive, which as the name suggests, can be shared freely without penalty. The final dataset contains 1000 songs, each annotated by a minimum of 10 subjects, which is larger than many currently available music emotion dataset. Nowadays, the amount of multimedia contents is explosively increasing and it is often a challenging problem to find a content that will be appealing or matches users' current mood or affective state. In order to achieve this goal, an effcient indexing technique should be developed to annotate multi-media contents such that these annotations can be used in a retrieval process using an appropriate query. One approach to such indexing techniques is to determine the affect(type and intensity), which can be induced in a user while consuming multimedia. In this paper, affective content analysis of music video clips is performed to determine the emotion they can induce in people. To this end, a subjective test was developed, where 32 participants watched different music video clips and assessed their induced emotions. These self assessments were used as ground-truth and the results of classification using audio, visual and audiovisual features extracted from music video clips are presented and compared. The goal of music mood regression is to represent the emotional expression of music pieces as numerical values in a low-dimensional mood space and automatically predict those values for unseen music pieces. Existing studies on this topic usually train and test regression models using music datasets sampled from the same culture source, annotated by people with the same cultural background, or otherwise constructed by the same method. In this study, we explore whether and to what extent regression models trained with samples in one dataset can be applied to predicting valence and arousal values of samples in another dataset. Specifically, three datasets that differ in factors such as cultural backgrounds of stimuli (music) and subjects (annotators), stimulus types and annotation methods are evaluated and the results suggested that cross-cultural and cross-dataset predictions of both valence and arousal values could achieve comparable performance to within-dataset predictions. We also discuss how the generalizability of regression models can be affected by dataset characteristics. Findings of this study may provide valuable insights into music mood regression for nonWestern and other music where training data are scarce.