We present our system description of input-level multimodal fusion of audio, video, and text for recognition of emotions and their intensities for the 2018 First Grand Challenge on Computational Modeling of Human Multimodal Language. Our proposed approach is based on input-level feature fusion with sequence learning from Bidirectional Long-Short Term Memory (BLSTM) deep neural networks (DNNs). We show that our fusion ap- proach outperforms unimodal predictors. Our sys- tem performs 6-way simultaneous classification and regression, allowing for overlapping emotion labels in a video segment. This leads to an over- all binary accuracy of 90%, overall 4-class accu- racy of 89.2% and an overall mean-absolute-error (MAE) of 0.12. Our work shows that an early fu- sion technique can effectively predict the presence of multi-label emotions as well as their coarse- grained intensities. The presented multimodal ap- proach creates a simple and robust baseline on this new Grand Challenge dataset. Furthermore, we provide a detailed analysis of emotion intensity distributions as output from our DNN, as well as a related discussion concerning the inherent diffi- culty of this task.
Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed by combining (fusing) the results of a separate audio and video classifier on the decision level. The video-classifier uses features based on the principal components of 20 tracked facial points, for audio we use the commonly used PLP and RASTA- PLP features. Our results indicate that RASTA-PLP features outperform PLP features for laughter detection in audio. We compared hidden Markov models (HMMs), Gaussian mix- ture models (GMMs) and support vector machines (SVM) based classifiers, and found that RASTA-PLP combined with a GMM resulted in the best performance for the audio modality. The video features classified using a SVM resulted in the best single-modality performance. Fusion on the decision-level resulted in laughter detection with a significantly better perfor- mance than single-modality classification.
Emotions and feelings not the same. Emotions are energy in motion; composite biological signals like a fast beating heart or sweaty palms. We are all experiencing emotions every single moment of every single day but we don’t necessarily feel them. Feelings are the awareness in our minds of the ‘energy in motion’. The energy is there, but we don’t necessarily feel it: we have not really learned to understand our own emotional life. To transform our lives, we have to understand that ultimately emotions will predict our health, personal sense of wellbeing, success, fulfilment, motivation and decisions. The good news is with awareness comes the ability to direct and manage our emotions – they are part of us, not something that is imposed on us.
Given the focus on the relational aspects of emotions in this section of the paper, it is important to highlight how music, emotions and research are themselves relational, and that any intersections between them should be scrutinised. On top of calls for ethnomusicological research to create opportunities for self-representation (Hofman, 2010) and generate "knowledge […] from a truly horizontal, intercultural dialogue and not through top-to-bottom neo-colonial systems of validation" (Arujo, 2008, p. 14), we must also navigate the affective relations that underpin music and research relationships in the field. Is there emotional intent behind our work, for instance, aiming to develop compassion or empathic imagination in research and music audiences? If so, how are participants and interlocutors positioned in relation to these aims? What role does empathy play in our research methodologies - even in seemingly collaborative focus group discussions or analyses - when rendering representations of musical or emotional experience? How is this empathy structured? The relationship between emotions and music raises several concerns for those working in peacebuilding contexts (as practitioners, researchers, or both). Many of these issues, including the ways in which researching sensitive topics involves emotion work (Dickson-Swift, James, Kippen and Liamputtong, 2009) that might require strategies for self-care (Rager, 2005), lie beyond the scope of this article's purview. In the next section we consider the sociopolitical infrastructures of emotion, building on the hierarchical dimensions of affective relations that have been our focus here.
Correlational design was used to study the interpersonal violence and emotional-competences among adult and aged women. Further, data has also been subjected to t-test and regression analysis. Total 400 women (N=200 adult women+ N=200 aged women) constitute sample of the study. Sub- factors of emotional competence i.e. adequate depth of feeling, adequate expression and control of emotions, ability to cope problem with emotions, ability to function with emotions, and encouragement of positive emotions tested. In order to observe prevalence and nature of violence faced by the participants a brief interview was also conducted besides, the tools were used to assess the interpersonal violence and emotional- competences of the adult and aged women as followed: INTERPERSONAL VIOLENCE SCALE: To assess the
Audio-video content such as television programmes and the like may be distributed to consumers via a variety of media. Traditional linear television programmes are broadcast over a broadcast network, whether terrestrial, cable or satellite and may be consumed by users by a variety of receiver devices. Such audio-video content includes the audio and video components and may also include additional content such as subtitles, audio description and other additional data. Such audio-video content may also be distributed via additional routes, in particular via on demand services such as on the internet. In the process of repurposing audio-video content for other distribution channels, though, additional content such as subtitles, audio description or other data or metadata is not routinely copied with the result that such additional content is not available on the version distributed by the additional distribution route. Similar issues can occur when audio-video content is re-versioned for broadcast.
Caruso et al. (2002), found that using emotions means making use of emotions and feelings to improve thinking and generate positive thoughts (Caruso et al., 2002). It's also expressed as the ability to exploit emotions to smoothen the progress of cognitive actions, such as enhancing the ability of judgment and problem solving (Salovey & Grewal, 2005). Such use of emotions brings flexibility in leader’s cognitive skills and so he can plan better and take customized decisions which are the features of a charismatic leader (Mayer, 1986). That means use of emotions has some association with charismatic leadership. So following hypothesis is suggested: H2: Using emotion has a positive significant relationship with charismatic leadership
A number of important attributes of carts can be seen from this illustration. First is the cart's number. Each cart in the Library gets assigned a unique number when it is created. This number can range between 000001 and 999999, and is the primary 'handle' by which both Rivendell and external systems (like traffic or music schedulers) refer to the cart. Very often, sites have specific rules concerning which types of audio (commercials, promos, music, etc) and macros get assigned which numbers. We'll cover this area in some detail when we discuss groups.
Cuando nos preguntan qué es eso del estéreo, respondemos: un sonido que no es mono y que se escu- cha por dos altavoces. Acertamos en la primera parte, ya que el sonido estéreo no es monoaural (abreviado mono), pero lo segundo es una verdad a medias. Que una grabación se escuche por dos altavoces no garantiza que estemos escuchando un sonido estereofónico. Un sonido estéreo es aquel que tiene un audio diferente por cada uno de los dos canales (izquierdo y derecho). Si escuchas cual- quier canción con unos audífonos y prestas mucha atención, percibirás por un oído cosas que no oyes por el otro. Hay instrumentos que suenan más por el izquierdo y coros de voces que oirás más altos por el derecho. Si cierras los ojos, “verás” la banda tocando. Los coristas a la derecha del escena- rio, el guitarrista a la izquierda y la vocalista, casi al centro. Con sólo escucharlos, creas una ima- gen del espacio donde la banda está tocando… ¡es como estar de verdad en el concierto!
variables (such as age and total service) of managers and supervisors; and their Emotional Intelligence. But the variables like gender, designation and qualification of managers and supervisors do not have significant relationship on emotional intelligence. Emotional Intelligence has four elements: perceiving emotions, using emotions, understanding emotions, and managing emotions. Emotional Intelligence helps the managers and supervisors to create a positive environment so that people are happy working together. The result of correlation coefficient and regression revealed that Emotional Intelligence of managers and supervisors is significantly related with interpersonal facilitation in an organization. The study results provide sufficient evidence for the fact that Emotional Intelligence, measured as a set of abilities is associated positively with interpersonal behaviours. By having clear understanding of Emotional Intelligence, managers and supervisors in Public Sector Enterprises can increase the job commitment, loyalty and growth of organization.
Personality can be defined as a dynamic and organised set of traits possessed by an individual that uniquely influences the individual’s motivations, cognition and behaviours in diverse situations (Udoudoh, 2012). Personality refers to the behaviours, emotions and thought patterns unique to an individual (McCrae & Costa, 1997). Personality measures could be reduced or classified under five dimensions of personality, which has been labelled the “Big Five” (Digman, 1990; Goldberg, 1990, 1992; McCrae & John, 1992). The dimensions which comprised the five factor model (FFM) of personality are openness to experience, conscientiousness, extraversion, agreeableness and neuroticism (Costa & McCrae, 1992a; Goldberg, 1990). The five dimensions of the Big Five model is referred to as OCEAN, representing openness to experience, conscientiousness, extraversion, agreeableness and neuroticism. The Big Five has been found to generalise at a systemic level (Costa, & McCrae, 1992a; McCrae & Costa, 1997; Salgado 1997). Researchers have posited that the Big Five traits have a genetic element and that the hereditary component appears to be substantial (Costa & McCrae, 1988; Digman, 1989; Jang, Livesley, & Vernon, 1996). Salgado (2002) posited that personality traits predict different facets of job performance and affects job outcomes such as organisational commitment and job satisfaction. The Big Five personality test measures the five dimensions of personality, including neuroticism, extraversion, agreeableness, conscientiousness, and openness to experience (Costa & McCrae, 1985; Mount & Barrick, 1995). The next paragraph would be used to explain a hypothetical score of the BIG Five personality test.
Emotions. The scale of the teachers’ experienced emotions at school consisted of seventeen emotions: Happiness, pleasure, pride, encouragement, confidence, calmness, not angry-angry, flow-not flow, cheerfulness, exciting, not irritated-irritated, hope, competence, not nervousness-nervousness, anxiety, en- thusiasm and not boredom-boredom. The teachers were asked to indicate the extent to which they usually experienced each of the above eighteen emotions at school during the current school year. The emotions had the form of adjectives, with the positive pole having the high score of 7 and the negative pole having the low score of 1 (e.g., happy 7 6 5 4 3 2 1 unhappy). The con- struction of the scale was based on previous similar re- searches (see Pekrun, Goetz, Frenzel, Barchfeld, & Perry, 2011; Schutz & DeCuir, 2002; Sutton & Wheatley, 2003; Weiner, 2001, 2005), and it is a valid and reliable research instrument in studying experienced emotions in education in Greek popula- tion (see Stephanou, 201; Stephanou, Kariotoglou, & Ntinas, 2011; Stephanou & Mastora, submitted). Cronbach’s alpha val- ue was .89.
engine, and car braking. For each audio event, 100 short au- dio clips, each with length 3–10 seconds, are selected from the SoundIdeas sound e ﬀ ects library as the training data. In the training stage, the training audio streams are seg- mented into overlapped frames, and the features described in Section 3 are extracted. Based on these features, a com- plete specification of HMM, which includes two model pa- rameters (model size and number of mixtures in each state) and three sets of probabilities (initial probability, observa- tion probability, and transition probability), are determined. The model size and initial probability could be decided by the clustering algorithm described in the previous subsec- tion, and the number of mixtures in each state is empirically set as four because it is insensitive to the system performance according to our experiments. The Baum-Welch algorithm is then applied to estimate the transition probabilities between states and the observation probabilities within each state. Fi- nally, four HMMs are constructed for the audio events we concern. Details of the HMM training process can be found in .
Social environment is known not to be ready to interact with a personality who is not willing to communicate with children and adults, avoids group events and who is not able “to get infected” with group emotions like laughter, joy, good mood, etc (Shulzhenko, 2010, p. 32). Therefore, a high level of emotional intel- ligence is an extremely important component of psychological characteristics of a personality, particularly specialists working with individuals of developmental disorders, since empathy implies a response of one personality to feelings of an- other and plays an important part in interpersonal interaction.
En esta pestaña es posible modifica el controlador de audio que se va a usar y la tasa de sampleo, además del metrónomo (Activado/desactivado) y su volúmen. También se pueden modificar otros parámetros como activar el modo "Jack Transport Slave", permitiendo que otro programa lo use como esclavo, "Activar salida por pista" que es útil cuando se desea agregar efectos a un instrumento individual por medio de jack-rack. Note el valor de "Polifonía": dependiendo de su CPU, puede desear cambiar el número de notas simultáneas máximo para evitar que hydrogen cause xruns.
Boyatzis et. Al. (2008) find in their study that emotional intelligence competencies can be developed in students. Mayer and Salovey (1993) have defined it as „Emotional intelligence is the ability to perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge, and to reflectively regulate emotions so as to promote emotional and intellectual growth.‟ Singh (2003) found that different professionals need different levels of emotional intelligence for success. In fact studies that have tracked people‟s level of emotional intelligence through the year shows that people get better and better in these capabilities as they grow more adept at handling their empathy and social adroitness (Goleman, 1998).
Emotions were measured using the Positive And Negative Affect Schedule (PANAS) (Watson et al., 1988). This schedule, provided in appendix B, consisted of ten positive and ten negative emotions, which could be scored using a 5-point Likert Scale (ranging from 1 “very slightly” to 5 “very much”). The positive emotions were: interested, excited, strong, enthusiastic, proud, alert, inspired, determined, attentive and active. The negative emotions were: distressed, upset, guilty, scared, hostile, irritable, ashamed, nervous, jittery, afraid. Previous research showed that the Dutch version of this schedule had sufficient validity and reliability (Peeters, Ponds, & Vermeeren, 1996). In this study, the Cronbach’s alpha was α = .88 on T0 and α = .91 on T1 for the positive affect scale and α = .84 on T0 and α = .90 on T1 for the negative affect scale. However, if the positive emotion excited was deleted, the Cronbach’s alpha would increase to α = 0.90 on T0 and α = 0.93 on T1.
New Audio Track – Esto crea una nueva Pista de Audio vacía. Este comando raramente es requerido, ya que la importación, grabación, y el mezclado automáticamente crean nuevas pistas cuando sea necesario. Pero puede utilizarlo para cortar o copiar datos de una pista existente y pegarlos a una pista vacía. Si esa pista estaba a una frecuencia no preestablecida, podría requerir del uso del Set Rate del menú desplegable Track para fijar la frecuencia de muestreo correcta.