This book presents advances in speech and music in the domain of audio signal processing. The book begins with introductory chapters on the basics of speech and music, and then proceeds to computational aspects of speech and music, including music information retrieval and spoken language processing. The authors discuss the intersection in the field of computer science, musicology and speech analysis, and how the multifaceted nature of speech and music information processing requires unique algorithms, systems using sophisticated signal processing, and machine learning techniques that better extract useful information. The authors discuss how a deep understanding of both speech and music in terms of perception, emotion, mood, gesture and cognition is essential for successful application. Also discussed is the overwhelming amount of data that has been generated across the world that requires efficient processing for better maintenance, retrieval, indexing and querying and how machine learning and artificial intelligence are most suited for these computational tasks. The book provides both technological knowledge and a comprehensive treatment of essential topics in speech and music processing.

Music informatics is an interdisciplinary research area that encompasses data driven approaches to the analysis, generation, and retrieval of music. In the era of big data, two goals weigh heavily on many research agendas in this area: (a) the identification of better features and (b) the acquisition of better training data. To this end, researchers have started to incorporate findings and methods from music cognition, a related but historically distinct research area that is concerned with elucidating the underlying mental processes involved in music-related behavior.

Language fundamentally defines and distinguishes us as humans, as members of society, and as individuals. As we go through life, our relationship with language and with learning shifts and changes, but it remains significant. This book is an up-to-date resource for graduate students and researchers in second language (L2) acquisition who are interested in language learning across the lifespan. The main goal is to survey and evaluate what is known about the linguistic-cognition-affect associations that occur in L2 learning from birth through senescence (passing through the stages of childhood, adolescence, adulthood, and third age), the extent to which L2 acquisition may be seen as contributing to healthy and active aging, the impact of the development of personalized, technology-enhanced communicative L2 environments, and how these phenomena are to be approached scientifically and methodologically. The effects of certain specific variables, such as gender, socio-economic background, and bilingualism are also analyzed, as we argue that chronological age does not determine the positioning of L2 learners across the lifespan: age is part of a complex web of social distinctions such as psychological and individual factors that intersect in the construction of a learner’s relative status and opportunities.