Real-time expressive internet communications

This research work "Real-time Expressive Internet Communications" focuses on two subjects: One is the investigation of methods of automatic emotion detection and visualisation under real-time Internet communication environment, the other is the analysis of the influences of presenting visualised emotion expressivei mages to Internet users. To detect emotion within Internet communication, the emotion communication process over the Internet needs to be examined. An emotion momentum theory was developed to illustrate the emotion communication process over the Internet communication. It is argued in this theory that an Internet user is within a certain emotion state, the emotion state is changeable by internal and external stimulus (e.g. a received chat message) and time; stimulus duration and stimulus intensity are the major factors influencing the emotion state. The emotion momentum theory divides the emotions expressed in Internet communication into three dimensions: emotion category, intensity and duration. The emotion momentum theory was implemented within a prototype emotion extraction engine. The emotion extraction engine can analyse input text in an Internet chat environment, detect and extract the emotion being communicated, and deliver the parameters to invoke an appropriate expressive image on screen to the every communicating user's display. A set of experiments were carried out to test the speed and the accuracy of the emotion extraction engine. The results of the experiments demonstrated an acceptable performance of the emotion extraction engine. The next step of this study was to design and implement an expressive image generator that generates expressive images from a single neutral facial image. Generated facial images are classified into six categories, and for each category, three different intensities were achieved. Users need to define only six control points and three control shapes to synthesise all the expressive images and a set of experiments were carried out to test the quality of the synthesised images. The experiment results demonstrated an acceptable recognition rate of the generated facial expression images. With the emotion extraction engine and the expressive image generator,a test platform was created to evaluate the influences of emotion visualisation in the Internet communication context. The results of a series of experiments demonstratedthat emotion visualisation can enhancethe users' perceived performance and their satisfaction with the interfaces. The contributions to knowledge fall into four main areas; firstly, the emotion momentum theory that is proposed to illustrate the emotion communication process over the Internet; secondly, the innovations built into an emotion extraction engine, which senses emotional feelings from textual messages input by Internet users; thirdly, the innovations built into the expressive image generator, which synthesises facial expressions using a fast approach with a user friendly interface; and fourthly, the identification of the influence that the visualisation of emotion has on human computer interaction

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

Music information retrieval: conceptuel framework, annotation and user behaviour

Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences. This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music. In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval. Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population. Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system. Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

first_pagesettingsOrder Article Reprints Open AccessArticle A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute by Muhammad Imran Saleem 1,2,*ORCID,Atif Siddiqui 3ORCID,Shaheena Noor 4ORCID,Miguel-Angel Luque-Nieto 1,2ORCID andPablo Otero 1,2ORCID 1 Telecommunications Engineering School, University of Malaga, 29010 Malaga, Spain 2 Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain 3 Airbus Defence and Space, UK 4 Department of Computer Engineering, Faculty of Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan * Author to whom correspondence should be addressed. Appl. Sci. 2023, 13(1), 453; https://doi.org/10.3390/app13010453 Received: 12 November 2022 / Revised: 22 December 2022 / Accepted: 26 December 2022 / Published: 29 December 2022 Download Browse Figures Versions Notes Abstract Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input and converted into text. The system is easy to use and low cost. (...)This research has been partially funded by Universidad de Málaga, Málaga, Spain