George Kalliris

Journalism in a state of flux: Journalists as agents of technology innovation and emerging news practices

The move to a networked media environment presents a range of challenges for journalistic roles, norms and daily practices. This article employs actor network theory to investigate how different actors negotiate and ultimately shape the manner in which the internet and related digital technologies are embedded in the newsroom. Findings suggest that professional culture – articulated in skills, ideas and practices – acts as a network that weakens the potential impact of technology towards innovation and audience-oriented models of journalism. The results point to the conclusion that the internet and related tools are seen as empowering journalists to do their (traditional) jobs better instead of moving on to the next stage built around a stronger commitment to capitalize on the growing sociotechnical potential.

Multi-label classification of music by emotion

This work studies the task of automatic emotion detection in music. Music may evoke more than one different emotion at the same time. Single-label classification and regression cannot model this multiplicity. Therefore, this work focuses on multi-label classification approaches, where a piece of music may simultaneously belong to more than one class. Seven algorithms are experimentally compared for this task. Furthermore, the predictive power of several audio features is evaluated using a new multi-label feature selection method. Experiments are conducted on a set of 593 songs with six clusters of emotions based on the Tellegen-Watson-Clark model of affect. Results show that multi-label modeling is successful and provide interesting insights into the predictive quality of the algorithms and features.

Investigation of broadcast-audio semantic analysis scenarios employing radio-programme-adaptive pattern classification

The present paper focuses on the investigation of various audio pattern classifiers in broadcast-audio semantic analysis, using radio-programme-adaptive classification strategies with supervised training. Multiple neural network topologies and training configurations are evaluated and compared in combination with feature-extraction, ranking and feature-selection procedures. Different pattern classification taxonomies are implemented, using programme-adapted multi-class definitions and hierarchical schemes. Hierarchical and hybrid classification taxonomies are deployed in speech analysis tasks, facilitating efficient speaker recognition/identification, speech/music discrimination, and generally speech/non-speech detection-segmentation. Exhaustive qualitative and quantitative evaluation is conducted, including indicative comparison with non-neural approaches. Hierarchical approaches offer classification-similarities for easy adaptation to generic radio-broadcast semantic analysis tasks. The proposed strategy exhibits increased efficiency in radio-programme content segmentation and classification, which is one of the most demanding audio semantics tasks. This strategy can be easily adapted in broader audio detection and classification problems, including additional real-world speech-communication demanding scenarios.

Novel wavelet domain Wiener filtering de-noising techniques: Application to bowel sounds captured by means of abdominal surface vibrations

Abstract This work focuses on the design and evaluation of efficient and accurate de-noising algorithms that combine robust signal enhancement and minimum signal distortion. The proposed method introduces novel, frequency depended, parametric, Wiener filtering techniques that involve Discrete Wavelet Transform and Wavelet Packets. Implementations of various decomposition schemes, different mother wavelets and various thresholding options were tested, while perceptual criteria were also taken into account. The introduced de-noising approach has been extensively tested on human bowel sounds, captured by means of abdominal surface vibration recordings, in order to be further utilized as a diagnostic tool. Qualitative and quantitative analysis of the methods performance, when applied to various types of recorded and synthetic sounds, revealed that the new approach works excellent with favourable results.

Joint wavelet video denoising and motion activity detection in multimodal human activity analysis: application to video-assisted bioacoustic/psychophysiological monitoring

The current work focuses on the design and implementation of an indoor surveillance application for long-term automated analysis of human activity, in a video-assisted biomedical monitoring system. Video processing is necessary to overcome noise-related problems, caused by suboptimal video capturing conditions, due to poor lighting or even complete darkness during overnight recordings. Modified wavelet-domain spatiotemporal Wiener filtering and motion-detection algorithms are employed to facilitate video enhancement, motion-activity-based indexing and summarization. Structural aspects for validation of the motion detection results are also used. The proposed system has been already deployed in monitoring of long-term abdominal sounds, for surveillance automation, motion-artefacts detection and connection with other psychophysiological parameters. However, it can be used to any video-assisted biomedical monitoring or other surveillance application with similar demands.

Web Third-person effect in structural aspects of the information on media websites

Sample from media websites, 9150 respondents, Greece.Factors that predict the Web Third-person effect (WTPE) were found.Age has statistically significant effects on WTPE. In this study, the characteristics of what users observe when visiting a media website as wells as the prediction of the impact on oneself, friends and others are researched. The influence that this information has over their opinion verifies the existence of Web Third-person effect (WTPE). With the use of an online survey (N=9150) in all media websites it was proved that the variables that have a greater impact either on others or our friends than ourselves are: The number of users being concurrently online on the same media website, the exact number of users having read each article on a media website as well as the number of users having shared a news article on facebook, twitter, or other social networks. Moreover, age is a significant factor that explains the findings and is important to the effect. Additionally, factors that affect the influence of the user generated messages on others than on oneself were found. Furthermore, the more credible the news is perceived to be and when there is not a particular mediated message the WTPE is absent confirming the existing theory.

Sound events and emotions: Investigating the relation of rhythmic characteristics and arousal

A variety of recent researches in Audio Emotion Recognition (AER) outlines high performance and retrieval accuracy results. However, in most works music is considered as the original sound content that conveys the identified emotions. One of the music characteristics that is found to represent a fundamental means for conveying emotions are the rhythm-related acoustic cues. Although music is an important aspect of everyday life, there are numerous non-linguistic and nonmusical sounds surrounding humans, generally defined as sound events (SEs). Despite this enormous impact of SEs to humans, a scarcity of investigations regarding AER from SEs is observed. There are only a few recent investigations concerned with SEs and AER, presenting a semantic connection between the former and the listeners triggered emotion. In this work we analytically investigate the connection of rhythm-related characteristics of a wide range of common SEs with the arousal of the listener using sound events with semantic content. To this aim, several feature evaluation and classification tasks are conducted using different ranking and classification algorithms. High accuracy results are obtained, demonstrating a significant relation of SEs rhythmic characteristics to the elicited arousal.

Investigating quality of Experience and Learning (QoE & QoL) of audiovisual content broadcasting to learners over IP networks

This paper investigates the implementation and validation of different qualities for live broadcasting of courses over the Internet. The main aim of this work is to identify the adequate balance between the required bandwidth for live broadcasting and the Quality of Experience and Learning (QoE & QoL). The data used in this study was acquired from real-world learning scenarios.

Computer‐aided systems for prolonged recording and analysis of human bowel sounds

This paper presents the design of noninvasive systems for the investigation of the small bowel motility patterns. The basic idea was to implement dedicated devices for the recording and analysis of human bowel sounds. Properly modified transducers were used for the caption of gastrointestinal sound signals. A data acquisition ambulatory system, consisting of a preamplifier and a recording unit, was initially designed capable for maximum continuous recording of 6 h. After the first experimental results with this system, the necessity for longer recording duration was obvious. For this reason the installation of a computer‐based stationary system in a special reformed examination room was the next development step. Triggering of the input data and rejection of the regions with absence of gastrointestinal activity allowed much more recording duration. A computerized signal processing system, which involves equalizing procedures, noise cancellation techniques, and neural network algorithms, was used for the o...

Towards Intelligent Cross-Media Publishing: Media Practices and Technology Convergence Perspectives

This chapter investigates technological issues that have arisen in implementing cross-media publishing. Specifically the various content types (text, pictures, audio, video, etc.) that are included in cross-media publishing require different management and prerequisites with respect to the media publishing channels and the involved terminals at both ends, production and consumer. A modular content documentation, selection and management model is proposed for intelligent cross-media publishing automation, taking advantage of contemporary semantic multimodal interaction, sophisticated meta-data processing and Web 2.0/3.0 trends.