Rafael Ramírez-Melendez - Böcker

Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.

Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.

For thousands of years, music has acted as a powerful medium for evoking emotions, facilitating communication, and nurturing overall well-being. With the advent of new sophisticated neuroimaging technology, human responses to music and music therapy are being viewed through a new lens. As a consequence, new knowledge is being obtained about how music can produce significant improvements in cognitive, social, overt and agitated behaviours. The aim of this book is to provide an overview of neurocognitive music therapy, its impact and implications in the practice of evidence-based music interventions. The book seeks to provide researchers, psychologists, music therapists, musicians and physicians interested in the therapeutic applications of music, with a source of information about current techniques and novel music interventions. It is structured into several chapters, each of them presenting peer-reviewed research and evidence-based procedures carried out in a specific clinical context. Topics covered in the book include:Musical engagement for individuals with motor disabilitiesEnhancing emotional processing in autism through musicStroke rehabilitation via musical interventionsMusical neurofeedback for emotional disordersEmotional modulation with music therapy in palliative careAI-driven personalisation in music interventionsThe book highlights the profound capacity of music-based interventions to facilitate cognitive and emotional processing, enhance communication, and promote motor rehabilitation. At the same time, the book demonstrates how modern technologies offer new opportunities to evaluate, validate, and potentiate music-based interventions, allowing new and innovative possibilities and more personalised interventions. This book aims to contribute to the growing body of knowledge in this field and inspire further research and innovation in the practice of music therapy.