EMIR: A novel emotion-based music retrieval system

Music is inherently expressive of emotion meaning and affects the mood of people. In this paper, we present a novel EMIR (Emotional Music Information Retrieval) System that uses latent emotion elements both in music and non-descriptive queries (NDQs) to detect implicit emotional association between users and music to enhance Music Information Retrieval (MIR). We try to understand the latent emotional intent of queries via machine learning for emotion classification and compare the performance of emotion detection approaches on different feature sets. For this purpose, we extract music emotion features from lyrics and social tags crawled from the Internet, label some for training and model them in high-dimensional emotion space and recognize latent emotion of users by query emotion analysis. The similarity between queries and music is computed by verified BM25 model

An Action Selection Architecture for an Emotional Agent

An architecture for action selection is presented linking emotion, cognition and behavior. It defines the information and emotion processes of an agent. The architecture has been implemented and used in a prototype environment

An emotional mess! Deciding on a framework for building a Dutch emotion-annotated corpus

Seeing the myriad of existing emotion models, with the categorical versus dimensional opposition the most important dividing line, building an emotion-annotated corpus requires some well thought-out strategies concerning framework choice. In our work on automatic emotion detection in Dutch texts, we investigate this problem by means of two case studies. We find that the labels joy, love, anger, sadness and fear are well-suited to annotate texts coming from various domains and topics, but that the connotation of the labels strongly depends on the origin of the texts. Moreover, it seems that information is lost when an emotional state is forcedly classified in a limited set of categories, indicating that a bi-representational format is desirable when creating an emotion corpus.Seeing the myriad of existing emotion models, with the categorical versus dimensional opposition the most important dividing line, building an emotion-annotated corpus requires some well thought-out strategies concerning framework choice. In our work on automatic emotion detection in Dutch texts, we investigate this problem by means of two case studies. We find that the labels joy, love, anger, sadness and fear are well-suited to annotate texts coming from various domains and topics, but that the connotation of the labels strongly depends on the origin of the texts. Moreover, it seems that information is lost when an emotional state is forcedly classified in a limited set of categories, indicating that a bi-representational format is desirable when creating an emotion corpus.P

Emotion recognition abilities and empathy of victims of bullying

Objectives: Bullying is a form of systematic abuse by peers with often serious consequences for victims. Few studies have considered the role of emotion recognition abilities and empathic behaviour for different bullying roles. This study investigated physical and relational bullying involvement in relation to basic emotion recognition abilities, and empathic styles in children. Using the framework of the Social Information Processing model, it was expected that victims would have poor emotion recognition abilities, and that bullies would demonstrate low levels of empathy. Methods: Data was collected from UK children (N = 373) aged 9-11 years who completed a bullying instrument, the Bryant Index of Empathy measurement, and the DANVA (Diagnostic Analysis of Nonverbal Accuracy) to assess emotion recognition abilities. Children were classified into physical and relational bullying roles (bully, victim, bully/victim neutral) for analytical purposes. Results: While physical victims, bullies and neutrals differed little in their emotion recognition abilities, relational victims were particularly poor in recognising negative emotions of anger and fear in faces. No differences were found in empathy scores, according to bullying roles. Conclusions: Children who are relationally victimised are poorer in understanding emotional information than bullies and non-involved children. In light of previous research that victims of bullying more frequently experience child abuse, future interventions should consider the importance of emotion and social skills training for these vulnerable children

Affective Music Information Retrieval

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 \emph{acoustic emotion Gaussians} (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.Comment: 40 pages, 18 figures, 5 tables, author versio