Just about everyone likes to listen to music to put them "in the mood," and these techniques get you "out" of a mood! The "Tao" part is about accepting what you're feeling, and dealing with it, by using Dr. Ortiz's methods. Includes musical menus that you can use to create your own program for dealing with issues, koans for meditation, and various other fun exercises to make music a part of your holistic health program. Appendix, bibliography, index.

The guitarists' guitarist and the songwriters' songwriter, the legendary Bert Jansch has influenced stars as diverse as Jimi Hendrix, Jimmy Page, Paul Simon, Sandy Denny, Nick Drake, Donovan, Pete Townshend, Neil Young, Bernard Butler, Beth Orton and Laura Marling. Unassuming, enigmatic and completely focused on his music until his death in October 2011, he remained singularly resilient to the vagaries of fashion, being rediscovered and revered by new generations of artists every few years. Born in Edinburgh in 1943, Jansch became an inspirational and pioneering figure during Britain's 'folk revival' of the 1960s. In 1967 he formed folk/jazz fusion band Pentangle with John Renbourn and enjoyed international success until they split in 1973, when he returned to a solo career. In Dazzling Stranger, Colin Harper looks at the career Jansch enjoyed, which has secured his standing as one of the true originals of British music.

The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing. The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining. The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.