Affective Recommendation of Movies Based on Selected Connotative Features

The apparent difficulty in assessing emotions elicited by movies and the undeniable high variability in subjects emotional responses to filmic content have been recently tackled by exploring film connotative properties: the set of shooting and editing conventions that help in transmitting meaning to the audience. Connotation provides an intermediate representation which exploits the objectivity of audiovisual descriptors to predict the subjective emotional reaction of single users. This is done without the need of registering users physiological signals neither by employing other people highly variable emotional rates, but just relying on the inter-subjectivity of connotative concepts and on the knowledge of users reactions to similar stimuli. This work extends previous by extracting audiovisual and film grammar descriptors and, driven by users rates on connotative properties, creates a shared framework where movie scenes are placed, compared and recommended according to connotation. We evaluate the potential of the proposed system by asking users to assess the ability of connotation in suggesting filmic content able to target their affective requests

Understanding Agreement and Disagreement in Listeners’ Perceived Emotion in Live Music Performance

Emotion perception of music is subjective and time dependent. Most computational music emotion recognition (MER) systems overlook time- and listener-dependent factors by averaging emotion judgments across listeners. In this work, we investigate the influence of music, setting (live vs lab vs online), and individual factors on music emotion perception over time. In an initial study, we explore changes in perceived music emotions among audience members during live classical music performances. Fifteen audience members used a mobile application to annotate time-varying emotion judgments based on the valence-arousal model. Inter-rater reliability analyses indicate that consistency in emotion judgments varies significantly across rehearsal segments, with systematic disagreements in certain segments. In a follow-up study, we examine listeners' reasons for their ratings in segments with high and low agreement. We relate these reasons to acoustic features and individual differences. Twenty-one listeners annotated perceived emotions while watching a recorded video of the live performance. They then reflected on their judgments and provided explanations retrospectively. Disagreements were attributed to listeners attending to different musical features or being uncertain about the expressed emotions. Emotion judgments were significantly associated with personality traits, gender, cultural background, and music preference. Thematic analysis of explanations revealed cognitive processes underlying music emotion perception, highlighting attributes less frequently discussed in MER studies, such as instrumentation, arrangement, musical structure, and multimodal factors related to performer expression. Exploratory models incorporating these semantic features and individual factors were developed to predict perceived music emotion over time. Regression analyses confirmed the significance of listener-informed semantic features as independent variables, with individual factors acting as moderators between loudness, pitch range, and arousal. In our final study, we analyzed the effects of individual differences on music emotion perception among 128 participants with diverse backgrounds. Participants annotated perceived emotions for 51 piano performances of different compositions from the Western canon, spanning various era. Linear mixed effects models revealed significant variations in valence and arousal ratings, as well as the frequency of emotion ratings, with regard to several individual factors: music sophistication, music preferences, personality traits, and mood states. Additionally, participants' ratings of arousal, valence, and emotional agreement were significantly associated to the historical time periods of the examined clips. This research highlights the complexity of music emotion perception, revealing it to be a dynamic, individual and context-dependent process. It paves the way for the development of more individually nuanced, time-based models in music psychology, opening up new avenues for personalised music emotion recognition and recommendation, music emotion-driven generation and therapeutic applications

The Warburg Dance Movement Library-The WADAMO Library: A Validation Study

The Warburg Dance Movement Library is a validated set of 234 video clips of dance movements for empirical research in the fields of cognitive science and neuroscience of action perception, affect perception and neuroaesthetics. The library contains two categories of video clips of dance movement sequences. Of each pair, one version of the movement sequence is emotionally expressive (Clip a), while the other version of the same sequence (Clip b) is not expressive but as technically correct as the expressive version (Clip a). We sought to complement previous dance video stimuli libraries. Facial information, colour and music have been removed, and each clip has been faded in and out. We equalised stimulus length (6 seconds, 8 counts in dance theory), the dancers’ clothing and video background and included both male and female dancers, and we controlled for technical correctness of movement execution. The Warburg Dance Movement Library contains both contemporary and ballet movements. Two online surveys (N = 160) confirmed the classification into the two categories of expressivity. Four additional online surveys (N = 80) provided beauty and liking ratings for each clip. A correlation matrix illustrates all variables of this norming study (technical correctness, expressivity, beauty, liking, luminance, motion energy)

A Connotative Space for Supporting Movie Affective Recommendation

The problem of relating media content to users’affective responses is here addressed. Previous work suggests that a direct mapping of audio-visual properties into emotion categories elicited by films is rather difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of video features and the subjective sphere of emotions, we propose to shift the representation towards the connotative properties of movies, in a space inter-subjectively shared among users. Consequently, the connotative space allows to define, relate and compare affective descriptions of film videos on equal footing. An extensive test involving a significant number of users watching famous movie scenes, suggests that the connotative space can be related to affective categories of a single user. We apply this finding to reach high performance in meeting user’s emotional preferences

Modelling affect for horror soundscapes

The feeling of horror within movies or games relies on the audience’s perception of a tense atmosphere — often achieved through sound accompanied by the on-screen drama — guiding its emotional experience throughout the scene or game-play sequence. These progressions are often crafted through an a priori knowledge of how a scene or game-play sequence will playout, and the intended emotional patterns a game director wants to transmit. The appropriate design of sound becomes even more challenging once the scenery and the general context is autonomously generated by an algorithm. Towards realizing sound-based affective interaction in games this paper explores the creation of computational models capable of ranking short audio pieces based on crowdsourced annotations of tension, arousal and valence. Affect models are trained via preference learning on over a thousand annotations with the use of support vector machines, whose inputs are low-level features extracted from the audio assets of a comprehensive sound library. The models constructed in this work are able to predict the tension, arousal and valence elicited by sound, respectively, with an accuracy of approximately 65%, 66% and 72%.peer-reviewe

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Opportunity Knocks: An Economic Analysis of Television Advertisements

Certain aspects of advertising–especially on television–are not easily explained with conventional economic models. In particular, much of the imagery and repetitive thematic content seen in advertisements suggests it is "psychological" in nature, as opposed to "informative". To understand the economic rationale for incorporating such material, we develop a theory of preferences in which information about threshold payoffs induces sudden shifts in demand. These threshold payoffs are best understood in the context of human evolutionary history. Furthermore, the presence of threshold payoffs in consumer preferences gives firms incentive for providing threshold-type information. To examine the use of threshold-related content in television advertisements, we look for this con- tent in a sample of 370 television advertisements. We find considerable evidence that advertisers make strategic use of threshold-type content in television advertisements. Specifically, threshold-related content occurred in 83% of food and beverage advertisements for children and in 71% of advertisements for general audiences. Furthermore, the threshold-related content in children’s food and beverage advertisements occurred with statistically greater frequency than factual content, which isn’t true for food and beverage advertisements for general audiences

A hybrid approach for item collection recommendations : an application to automatic playlist continuation

Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction. In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience. Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version