@article{KotthausSchaeferStankovicetal.2021,
  author    = {Kotthaus, Jochem and Sch{\"a}fer, Matthias and Stankovic, Nikola and Weitzel, Gerrit},
  title     = {How soccer becomes politics},
  series = {International journal of sport communication},
  volume    = {14},
  journal   = {International journal of sport communication},
  number    = {3},
  publisher = {Human Kinetics Publ.},
  address   = {Champaign},
  issn      = {1936-3915},
  doi       = {10.1123/ijsc.2020-0320},
  pages     = {428 -- 447},
  year      = {2021},
  abstract  = {In this case study, the authors elaborate on the narrative structure of transnational popular media events. Drawing from Dayan and Katz's concept of media events and Julia Sonnevend's exceptional work on iconic global media events, they argue that fundamental changes in the way occurrences are being reported on and news is structured must be considered. Allowing for recent technological advancements, the role of the consumer and the compression of time in media use, the authors develop a methodological and theoretical framework fitting a more mundane and everyday life-based approach. They derive their results from the analysis of the "Podgorica Media Event," a news cycle emerging from a racist incident during an international soccer game between England and Montenegro. Based on the body of 250 international news pieces, they identify a primary mother narration and a distinctive narration as the typical ways of storytelling on a transnational level. While differing greatly in content, aspects of transnational popular media events serve to protect and reify the cultural background they are grounded in on a national level. Thus, we assume that sport, or, more specifically, soccer, may become political in media communication not by the impact of state government but by the consumers themselves choosing and developing a popular media event in the first place.},
  language  = {en}
}
@article{HeckerSteckhanEybenetal.2022,
  author    = {Hecker, Pascal and Steckhan, Nico and Eyben, Florian and Schuller, Bj{\"o}rn Wolfgang and Arnrich, Bert},
  title     = {Voice Analysis for Neurological Disorder Recognition - A Systematic Review and Perspective on Emerging Trends},
  series = {Frontiers in Digital Health},
  journal   = {Frontiers in Digital Health},
  publisher = {Frontiers Media SA},
  address   = {Lausanne, Schweiz},
  issn      = {2673-253X},
  doi       = {10.3389/fdgth.2022.842301},
  pages     = {16},
  year      = {2022},
  abstract  = {Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.},
  language  = {en}
}
@misc{HeckerSteckhanEybenetal.2022,
  author    = {Hecker, Pascal and Steckhan, Nico and Eyben, Florian and Schuller, Bj{\"o}rn Wolfgang and Arnrich, Bert},
  title     = {Voice Analysis for Neurological Disorder Recognition - A Systematic Review and Perspective on Emerging Trends},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {13},
  doi       = {10.25932/publishup-58101},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-581019},
  pages     = {16},
  year      = {2022},
  abstract  = {Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.},
  language  = {en}
}