Affective computing in the context of music therapy: a systematic review

Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

DIAGNOSTICS OF THE LEVEL OF EMOTIONAL INTELLIGENCE DEVELOPMENT OF PRESCHOOL CHILDREN

The purpose of this study is to identify the level of development of emotional intelligence of preschool children. The article notes that in modern conditions, the problem of the development of the emotional intelligence of pre-schoolers is relevant, this is due to the fact that the process of globalization, changes in all spheres of life had a negative impact on the pre-schooler, subjected him to emotional tests that inhibit the development of emotional intelligence of preschool children. The author of the article proves the importance of the development of emotional intelligence in preschool children. In his opinion, the preschool age has great opportunities for the development of the emotional intelligence of preschool children. The main goal of the study was to identify the level of development of emotional intelligence in preschool children. The study involved 40 pre-schoolers of diverse ages: 3-4 years old, 4-5 years old, and 5-6 years old. The indicators for the development of the emotional intelligence of pre-schoolers are: knowledge of various types of emotions, recognition of emotions, description of various emotions, identification of the causes of the appearance of emotions, awareness of their own and others' emotions, the ability to manage their own emotional state and the emotions of other people. The above indicators helped to identify the following levels of development of the emotional intelligence of preschool children: high, medium and low. The results of the study of the level of development of emotional intelligence indicate that: - pre-schoolers have a very poorly formed emotional intelligence, there are no emotional and motivational attitudes towards themselves, people around them, peers, - there are a lot of preschool children with a low and medium level of development of emotional intelligence, there are practically no children with a high level of development of emotional intelligence, - children have deficiently developed communication skills in different life situations with peers, adults, - the level of development of emotional intelligence in pre-schoolers is higher at the age of 6-7 years, very low at the age of three, i.e. the age of children, as well as temperament, influences, sanguine people have a high level of development of emotional intelligence, girls, compared to boys, have a high level of development of emotional intelligence.

Applying Multitask Deep Learning to Emotion Recognition in Speech

Purpose of research. Emotions play one of the key roles in the regulation of human behaviour. Solving the problem of automatic recognition of emotions makes it possible to increase the effectiveness of operation of a whole range of digital systems such as security systems, human-machine interfaces, e-commerce systems, etc. At the same time, the low efficiency of modern approaches to recognizing emotions in speech can be noted. This work studies automatic recognition of emotions in speech applying machine learning methods.Methods. The article describes and tests an approach to automatic recognition of emotions in speech based on multitask learning of deep convolution neural networks of AlexNet and VGG architectures using automatic selection of the weight coefficients for each task when calculating the final loss value during learning. All the models were trained on a sample of the IEMOCAP dataset with four emotional categories of ‘anger’, ‘happiness’, ‘neutral emotion’, ‘sadness’. The log-mel spectrograms of statements processed by a specialized algorithm are used as input data.Results. The considered models were tested on the basis of numerical metrics: the share of correctly recognized instances, accuracy, completeness, f-measure. For all of the above metrics, an improvement in the quality of emotion recognition by the proposed model was obtained in comparison with the two basic single-task models as well as with known solutions. This result is achieved through the use of automatic weighting of the values of the loss functions from individual tasks when forming the final value of the error in the learning process.Conclusion. The resulting improvement in the quality of emotion recognition in comparison with the known solutions confirms the feasibility of applying multitask learning to increase the accuracy of emotion recognition models. The developed approach makes it possible to achieve a uniform and simultaneous reduction of errors of individual tasks, and is used in the field of emotions recognition in speech for the first time.

Recognition of emotions through facial expression analysis

The possibility of recognizing what emotion one of our peers is experiencing has been the subject of study by various researchers over the years, Paul Ekman being the one who has delved most deeply into this subject, the most viable and simple way to achieve this would be through the analysis of people's facial expressions. The search for information was carried out using rigorous exclusion criteria such as studies corresponding to grizzly data and letters to the editor, and inclusion criteria such as studies published only in high impact journals such as PubMed, Elsevier, Taylor & Francis, ScienceDirect, APA PsycNet and Springer, PRISMA guidelines and AMSTAR check-list were used. The main objective of this systematic review was to determine whether there is sufficient scientific literature evidence to clarify whether it is possible to accurately identify the six basic universal emotions "happiness, surprise, sadness, anger, fear and disgust" proposed by Paul Ekman through facial expressions. After the analysis of the articles collected and based on the main findings, it is concluded that the recognition of emotions through facial expressions is a subject that still needs to be studied in greater depth, as suggested by the results obtained.

Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

Implementation of an Intervention Plan for Emotional Development in People with Down Syndrome

The importance of music, as well as the different and diverse possibilities that it offers, favors the emotional development of any person. This research is based on the development and application of a set of activities, whose transversal axis is the use of music, to favor and promote the emotional development of people with Down syndrome. This application of activities was developed with a group of eight participants, between the ages of twenty and forty-five years old. Additionally, under a total duration of eight working sessions. In these sessions, listening, vocal, instrumental, and movement activities were developed. For each of the emotions worked on; joy, fear, anger, sadness, calm, and love, a story and a song from the story were selected for each one of them. The methodology used was qualitative, using program evaluation. For this purpose, on the one hand, the data obtained during the different sessions were analyzed, and on the other hand, the data collected in the two discussion groups carried out were analyzed. Finally, the data obtained were organized into six categories: image recognition, observation of emotions, experience of emotions, identification of emotions, recognition of emotions, and finally, enjoyment of emotions. It could be seen that, after the sessions, there was a significant improvement in the different categories. However, in the categories of identification of emotions and recognition of emotions, the results were more favorable compared to the rest.