Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression

Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance

Brain Music : Sistema generativo para la creación de música simbólica a partir de respuestas neuronales afectivas

gráficas, tablasEsta tesis de maestría presenta una metodología de aprendizaje profundo multimodal innovadora que fusiona un modelo de clasificación de emociones con un generador musical, con el propósito de crear música a partir de señales de electroencefalografía, profundizando así en la interconexión entre emociones y música. Los resultados alcanzan tres objetivos específicos: Primero, ya que el rendimiento de los sistemas interfaz cerebro-computadora varía considerablemente entre diferentes sujetos, se introduce un enfoque basado en la transferencia de conocimiento entre sujetos para mejorar el rendimiento de individuos con dificultades en sistemas de interfaz cerebro-computadora basados en el paradigma de imaginación motora. Este enfoque combina datos de EEG etiquetados con datos estructurados, como cuestionarios psicológicos, mediante un método de "Kernel Matching CKA". Utilizamos una red neuronal profunda (Deep&Wide) para la clasificación de la imaginación motora. Los resultados destacan su potencial para mejorar las habilidades motoras en interfaces cerebro-computadora. Segundo, proponemos una técnica innovadora llamada "Labeled Correlation Alignment"(LCA) para sonificar respuestas neurales a estímulos representados en datos no estructurados, como música afectiva. Esto genera características musicales basadas en la actividad cerebral inducida por las emociones. LCA aborda la variabilidad entre sujetos y dentro de sujetos mediante el análisis de correlación, lo que permite la creación de envolventes acústicos y la distinción entre diferente información sonora. Esto convierte a LCA en una herramienta prometedora para interpretar la actividad neuronal y su reacción a estímulos auditivos. Finalmente, en otro capítulo, desarrollamos una metodología de aprendizaje profundo de extremo a extremo para generar contenido musical MIDI (datos simbólicos) a partir de señales de actividad cerebral inducidas por música con etiquetas afectivas. Esta metodología abarca el preprocesamiento de datos, el entrenamiento de modelos de extracción de características y un proceso de emparejamiento de características mediante Deep Centered Kernel Alignment, lo que permite la generación de música a partir de señales EEG. En conjunto, estos logros representan avances significativos en la comprensión de la relación entre emociones y música, así como en la aplicación de la inteligencia artificial en la generación musical a partir de señales cerebrales. Ofrecen nuevas perspectivas y herramientas para la creación musical y la investigación en neurociencia emocional. Para llevar a cabo nuestros experimentos, utilizamos bases de datos públicas como GigaScience, Affective Music Listening y Deap Dataset (Texto tomado de la fuente)This master’s thesis presents an innovative multimodal deep learning methodology that combines an emotion classification model with a music generator, aimed at creating music from electroencephalography (EEG) signals, thus delving into the interplay between emotions and music. The results achieve three specific objectives: First, since the performance of brain-computer interface systems varies significantly among different subjects, an approach based on knowledge transfer among subjects is introduced to enhance the performance of individuals facing challenges in motor imagery-based brain-computer interface systems. This approach combines labeled EEG data with structured information, such as psychological questionnaires, through a "Kernel Matching CKA"method. We employ a deep neural network (Deep&Wide) for motor imagery classification. The results underscore its potential to enhance motor skills in brain-computer interfaces. Second, we propose an innovative technique called "Labeled Correlation Alignment"(LCA) to sonify neural responses to stimuli represented in unstructured data, such as affective music. This generates musical features based on emotion-induced brain activity. LCA addresses variability among subjects and within subjects through correlation analysis, enabling the creation of acoustic envelopes and the distinction of different sound information. This makes LCA a promising tool for interpreting neural activity and its response to auditory stimuli. Finally, in another chapter, we develop an end-to-end deep learning methodology for generating MIDI music content (symbolic data) from EEG signals induced by affectively labeled music. This methodology encompasses data preprocessing, feature extraction model training, and a feature matching process using Deep Centered Kernel Alignment, enabling music generation from EEG signals. Together, these achievements represent significant advances in understanding the relationship between emotions and music, as well as in the application of artificial intelligence in musical generation from brain signals. They offer new perspectives and tools for musical creation and research in emotional neuroscience. To conduct our experiments, we utilized public databases such as GigaScience, Affective Music Listening and Deap DatasetMaestríaMagíster en Ingeniería - Automatización IndustrialInvestigación en Aprendizaje Profundo y señales BiológicasEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale

Advances in Emotion Recognition: Link to Depressive Disorder

Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social text on social network platform. Recognition of emotion pattern based on explicit and implicit features extracted through wearable and other devices could be decoded through computational modeling. Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder. The chapter aims to summarize the main findings in the area of affective recognition and its applications in major depressive disorder (MDD), which have made rapid progress in the last decade

The conflict escalation resolution (CONFER) database

Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audiovisual recordings of televised political debates where conflicts naturally arise, and b)reporting baseline experiments on audiovisual conflict intensity estimation. The database contains approximately 142min of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject- and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict classification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/. The Conflict Escalation Resolution (CONFER) Database is presented.CONFER contains 142min (120 episodes) of recordings in Greek language.Episodes are extracted from TV political debates where conflicts naturally arise.Experiments are the first approach to continuous estimation of conflict intensity.Performance of various audio and visual features and classifiers is evaluated

Music emotion recognition: a multimodal machine learning approach

Music emotion recognition (MER) is an emerging domain of the Music Information Retrieval (MIR) scientific community, and besides, music searches through emotions are one of the major selection preferred by web users. As the world goes to digital, the musical contents in online databases, such as Last.fm have expanded exponentially, which require substantial manual efforts for managing them and also keeping them updated. Therefore, the demand for innovative and adaptable search mechanisms, which can be personalized according to users’ emotional state, has gained increasing consideration in recent years. This thesis concentrates on addressing music emotion recognition problem by presenting several classification models, which were fed by textual features, as well as audio attributes extracted from the music. In this study, we build both supervised and semisupervised classification designs under four research experiments, that addresses the emotional role of audio features, such as tempo, acousticness, and energy, and also the impact of textual features extracted by two different approaches, which are TF-IDF and Word2Vec. Furthermore, we proposed a multi-modal approach by using a combined feature-set consisting of the features from the audio content, as well as from context-aware data. For this purpose, we generated a ground truth dataset containing over 1500 labeled song lyrics and also unlabeled big data, which stands for more than 2.5 million Turkish documents, for achieving to generate an accurate automatic emotion classification system. The analytical models were conducted by adopting several algorithms on the crossvalidated data by using Python. As a conclusion of the experiments, the best-attained performance was 44.2% when employing only audio features, whereas, with the usage of textual features, better performances were observed with 46.3% and 51.3% accuracy scores considering supervised and semi-supervised learning paradigms, respectively. As of last, even though we created a comprehensive feature set with the combination of audio and textual features, this approach did not display any significant improvement for classification performanc

Computer audition for emotional wellbeing

This thesis is focused on the application of computer audition (i. e., machine listening) methodologies for monitoring states of emotional wellbeing. Computer audition is a growing field and has been successfully applied to an array of use cases in recent years. There are several advantages to audio-based computational analysis; for example, audio can be recorded non-invasively, stored economically, and can capture rich information on happenings in a given environment, e. g., human behaviour. With this in mind, maintaining emotional wellbeing is a challenge for humans and emotion-altering conditions, including stress and anxiety, have become increasingly common in recent years. Such conditions manifest in the body, inherently changing how we express ourselves. Research shows these alterations are perceivable within vocalisation, suggesting that speech-based audio monitoring may be valuable for developing artificially intelligent systems that target improved wellbeing. Furthermore, computer audition applies machine learning and other computational techniques to audio understanding, and so by combining computer audition with applications in the domain of computational paralinguistics and emotional wellbeing, this research concerns the broader field of empathy for Artificial Intelligence (AI). To this end, speech-based audio modelling that incorporates and understands paralinguistic wellbeing-related states may be a vital cornerstone for improving the degree of empathy that an artificial intelligence has. To summarise, this thesis investigates the extent to which speech-based computer audition methodologies can be utilised to understand human emotional wellbeing. A fundamental background on the fields in question as they pertain to emotional wellbeing is first presented, followed by an outline of the applied audio-based methodologies. Next, detail is provided for several machine learning experiments focused on emotional wellbeing applications, including analysis and recognition of under-researched phenomena in speech, e. g., anxiety, and markers of stress. Core contributions from this thesis include the collection of several related datasets, hybrid fusion strategies for an emotional gold standard, novel machine learning strategies for data interpretation, and an in-depth acoustic-based computational evaluation of several human states. All of these contributions focus on ascertaining the advantage of audio in the context of modelling emotional wellbeing. Given the sensitive nature of human wellbeing, the ethical implications involved with developing and applying such systems are discussed throughout

The conflict escalation resolution (CONFER) database

Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audiovisual recordings of televised political debates where conflicts naturally arise, and b)reporting baseline experiments on audiovisual conflict intensity estimation. The database contains approximately 142min of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject- and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict classification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/. The Conflict Escalation Resolution (CONFER) Database is presented.CONFER contains 142min (120 episodes) of recordings in Greek language.Episodes are extracted from TV political debates where conflicts naturally arise.Experiments are the first approach to continuous estimation of conflict intensity.Performance of various audio and visual features and classifiers is evaluated