5,698 research outputs found

    Confidence Intervals for ASR-based TTS Evaluation

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    The Power of Music

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    There is accruing evidence which indicates that actively making music can contribute to the enhancement of a range of non-musical skills and lead to other beneficial outcomes

    Music in Health and Diseases

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    It is well recognized that music is a unique and cost-effective solution for the rehabilitation of patients with cognitive deficits. However, music can also be used as a non-invasive and non-pharmacological intervention modality not only for the management of various disease conditions but also for maintaining good health overall. Music-based therapeutic strategies can be used as complementary methods to existing diagnostic approaches to manage cognitive deficits as well as clinical and physiological abnormalities of individuals in need. This book focuses on various aspects of music and its role in enhancing health and recovering from a disease. Chapters explore music as a healing method across civilizations and measure the effect of music on human physiology and functions

    Language in Action

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    A review of "Reinterpreting Gesture as Language. Language 'in Action'"

    Automatic emotion recognition in clinical scenario: a systematic review of methods

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    none4Automatic emotion recognition has powerful opportunities in the clinical field, but several critical aspects are still open, such as heterogeneity of methodologies or technologies tested mainly on healthy people. This systematic review aims to survey automatic emotion recognition systems applied in real clinical contexts, to deeply analyse clinical and technical aspects, how they were addressed, and relationships among them. The literature review was conducted on: IEEEXplore, ScienceDirect, Scopus, PubMed, ACM. Inclusion criteria were the presence of an automatic emotion recognition algorithm and the enrollment of at least 2 patients in the experimental protocol. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Moreover, the works were analysed according to a reference model to deeply examine both clinical and technical topics. 52 scientific papers passed inclusion criteria. Most clinical scenarios involved neurodevelopmental, neurological and psychiatric disorders with the aims of diagnosing, monitoring, or treating emotional symptoms. The most adopted signals are video and audio, while supervised shallow learning is mostly used for emotion recognition. A poor study design, tiny samples, and the absence of a control group emerged as methodological weaknesses. Heterogeneity of performance metrics, datasets and algorithms challenges results comparability, robustness, reliability and reproducibility.openPepa, Lucia; Spalazzi, Luca; Capecci, Marianna; Ceravolo, Maria GabriellaPepa, Lucia; Spalazzi, Luca; Capecci, Marianna; Ceravolo, Maria Gabriell

    Music Listening, Music Therapy, Phenomenology and Neuroscience

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    Models and Analysis of Vocal Emissions for Biomedical Applications

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    The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

    Musical organics: a heterarchical approach to digital organology

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    Gaining a comprehensive understanding of new musical technologies is fraught with difficulties. The digital materials from which they are formed are of such diverse origins and nature, that they do not match traditional organological classifications. This article traces the history of musical instrument classifications relevant to the understanding of new instruments, and proposes an alternative approach to the centuries-old tree-structure of downwards divisions. The proposed musical organics is a multi-dimensional, heterarchical, and organic approach to the analysis and classification of both traditional and new musical instruments that suits the rhizomatic nature of their material design and technical origins. Outlines of a hypothetical organological informatics retrieval system are also presented
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