In addition, this will allow to reduce the costs of audiovisual production and to overcome the limitation of classical marketing models based on surveys, opening also new paths to personalize the audiovisual consumer experience. The outcomes of this project would have a very positive effect, as they imply the integration of different study areas to endow machines with a new perspective when judging multimedia elements, not based solely on their technical characteristics, but also in their aesthetic valueĪnd the emotion they produce. To achieve this, we will identify the relevant elements of the audiovisual production in two channels: video and sound, and will study their correspondence with the perception of the spectators in the lines described earlier, generating an automatic prediction system. However, the factors that determine if they have a positive or negative effect (or no effect at all) on the audience’s response are still unknown to a great extent, and their corresponding analysis is far from automatic. arouse a particular emotional state) in the audience, which are the typical objectives of every attract attention) and affective responses (e.g. Regarding the automatic inference of the spectator’s perception, the audiovisual content may provoke cognitive effects (e.g. We will establish techniques to evaluate the impact on the users through the study of the popularity, sharing patterns, ratings and commentaries received, as a way of quantifying the effect of the On the other hand, the natural reaction will also be studied focusing on the audience’s behaviour on social networks by means of automaticĪnalysis of the metadata corresponding to the multimedia elements watched. Opinion surveys to a more precise model that will also allow measuring reactions to specific passages (not only the general reaction). This will allow transitioning from the current scenario only based on On the one hand, we will study the cognitive and emotional response of the spectators while they are watching the audiovisual, using neuroscience techniques and biometric measures based on sensors. We are going to work mainly in two response types: the immediate reaction while viewing the audiovisual, and the shown reaction on social networks. Our contribution will be based in combining these technologies to study thoroughly and predict the reactions of spectators to multimedia elements across different With this aim, we have gathered together different knowledge areas including: audiovisual communication, computer vision, multimodal systems, biometric sensors, social network analysis, and affective computing. We propose the automatic analysis of the relation between the audiovisual characteristics of a multimedia production and the impact caused in its audience. In this sense, a proble m that has recently attracted the interest of the scientific community is the development of models for objectivizing what is subjective, concretely, to measure the quality and impact of audiovisual productions. In a world where new technologies are progressively more related with multimedia information, it is essential to have tools that simplify the treatment of this type of data. Testing of a 787-word task achieved 92% correct sentence understanding with written input and 72% with continuous speech, speaker-independent, telephone-bandwidth spoken input. The training procedure learns both the network structure and the rules for generating the semantic representation of sentences. An automatic method for training such networks starting from a corpus of semantically annotated sentences is described. An approach is described that keeps the network size small by avoiding detailed modeling of linguistic constructs not essential for understanding the meaning of sentences. However, the application to real-world problems is difficult because the network sizes grow very large, and their training requires grammatical inference methods. The representation of language models through stochastic finite state networks offers several attractive features for speech recognition and understanding, including the ease of integration with the algorithms used for acoustic-phonetic decoding.
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