Many different disciplines are analyzing the impact of music today. How and why this ancient cultural asset molds, empowers and makes use of us can only become apparent in a synopsis and exchange involving scientific research. With this perspective as its foundation, the conference "Mozart and Science" extended invitations to the first interdisciplinary and international dialogue between the social and physical sciences about the effects of music. This book is based on the results of that congress. It contains contributions penned by leading scientists from around the world belonging to diverse music science disciplines and in particular covers psycho-physiological, neuro-developmental and cognitive aspects associated with the experience of music. Additional essays provide insights into research conducted about how music is applied in therapy and medicine.

This book conducts effective research on data-driven Structural Health Monitoring (SHM), and accordingly presents many novel feature extraction methods by time series analysis and signal processing, to extract reliable damage sensitive features from vibration responses. In this regard, some limitations of time series modeling are dealt with. For decision-making, innovative distance-based novelty detection techniques are presented to detect, locate, and quantify different damage scenarios. The performance of the presented methods is demonstrated via laboratory and full-scale structures along with several comparative studies. The main target audience of the book includes scholars, graduate students working on SHM via statistical pattern recognition in terms of feature extraction and classification for damage diagnosis under environmental and operational variations; it would also be beneficial for practicing engineers whose work involves these topics.

The book addresses the issue of interdisciplinary understanding of collaboration on the topic of social network studies. Researchers and practitioners from various disciplines including sociology, computer science, socio-psychology, public health, complex systems, and management science have worked largely independently, each with quite different principles, terminologies, theories. and methodologies. The book aims to fill the gap among these disciplines with a number of the latest interdisciplinary collaboration studies.

This collection of 13 selected papers originates from the International Seminar on Local Pattern Detection, held in Dagstuhl Castle, Germany in April 2004. This state-of-the-art survey on the emerging field Local Pattern Detection addresses four main topics. Three papers cover frequent set mining, four cover subgroup discovery, three cover the statistical view, and three papers are devoted to time phenomena.

Music processing in severely brain-injured patients with disorders of consciousness has been an emergent field of interest for over 30 years, spanning the disciplines of neuroscience, medicine, the arts and humanities. Disorders of consciousness (DOC) is an umbrella term that encompasses patients who present with disorders across a continuum of consciousness including people who are in a coma, in vegetative state (VS)/have unresponsive wakefulness syndrome (UWS), and in minimally conscious state (MCS). Technological developments in recent years, resulting in improvements in medical care and technologies, have increased DOC population numbers, the means for investigating DOC, and the range of clinical and therapeutic interventions under validation. In neuroimaging and behavioural studies, the auditory modality has been shown to be the most sensitive in diagnosing awareness in this complex population. As misdiagnosis remains a major problem in DOC, exploring auditory responsiveness and processing in DOC is, therefore, of central importance to improve therapeutic interventions and medical technologies in DOC. In recent years, there has been a growing interest in the role of music as a potential treatment and medium for diagnosis with patients with DOC, from the perspectives of research, clinical practice and theory. As there are almost no treatment options, such a non-invasive method could constitute a promising strategy to stimulate brain plasticity and to improve consciousness recovery. It is therefore an ideal time to draw together specialists from diverse disciplines and interests to share the latest methods, opinions, and research on this topic in order to identify research priorities and progress inquiry in a coordinated way. This Research Topic aimed to bring together specialists from diverse disciplines involved in using and researching music with DOC populations or who have an interest in theoretical development on this topic. Specialists from the following disciplines participated in this special issue: neuroscience; medicine; music therapy; clinical psychology; neuromusicology; and cognitive neuroscience.

This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.