text: 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.
  @cite_61: 
  @cite_9: It has been realized in the music emotion recognition (MER) community that personal difference, or individuality, has significant impact on the success of an MER system in practice. However, no previous work has explicitly taken individuality into consideration in an MER system. In this paper, the group-wise MER approach (GWMER) and personalized MER approach (PMER) are proposed to study the role of individuality. GWMER evaluates the importance of each individual factor such as sex, personality, and music experience, whereas PMER evaluates whether the prediction accuracy for a user is significantly improved if the MER system is personalized for the user. Experimental results demonstrate the effect of personalization and suggest the need for a better representation of individuality and for better prediction accuracy.
  @cite_42: With the development of multimedia technology, research on music is getting more and more popular. Nowadays researchers focus on studying the relationship between music and listeners’ emotions but they didn’t consider users’ differences. Therefore, we propose a Personalized Music Emotion Prediction (P-MEP) System to assist predicting listeners’ music emotion concerning with users’ differences. To analyze listeners’ emotional response to music, the P-MEP rules will be generated in the analysis procedure consisting of 5 phases. During the application procedure, the P-MEP System predicts the new listener’s emotional response to music. The result of the experiment shows that the generated P-MEP rules can be used to predict emotional response to music concerning with listeners’ differences.
  @cite_65: In this paper, aimed at emotional interactive needs in music retrieval, a new retrieval method, based on dynamic characteristics of fuzzy psychological and linguistic value computing of music emotion, is putting forward. And an improved interactive genetic algorithm is designed for the emotional retrieval goal. In order to reduce users' fatigue of retrieval process, choice operator for fine breed, crossover operator holding the dominant element of emotion vector, and self-genetic operation strategy are designed.
summary: A different methodology to address the subjectivity is to call for a user-dependent model trained on annotations of a specific user to personalize the emotion prediction @cite_42 @cite_9 @cite_65 . In @cite_9 , two personalization methods are proposed; the first trains a MER system for each individual specifically, whereas the second groups users according to some personal factors (e.g. gender, music experience, and personality) and then trains MER system for each user group. Another personalization scheme has also been studied @cite_61 : the first stage estimates the general perception of a music piece, whereas the second one predicts the difference between the general perception and the personal one of the target user.