"Modern information society is experiencing an explosion of digital content, comprising text, speech, video and graphics. The challenge is to organize, understand, and search multimodal information in a robust, efficient and intelligent manner. The present monograph significantly advances the state of the art and introduces novel concepts and algorithms for content-based analysis and retrieval for music data (Part I) and motion data (Part II).
Each part is suitable for use as stand-alone lecture notes for a graduate course in Computer Science. The monograph skillfully highlights the interaction between modeling, experimentation, and mathematical theory while introducing the students to current research fields."
Hans-Peter Seidel, Head of Computer Graphics Department, Max-Planck-Institut, Saarbrücken, Germany
"This book is a valuable contribution to the field. It introduces many new results in two upcoming areas within multimedia retrieval: audio and motion retrieval. In both parts of the book, efficiency and robustness are key issues, and indeed vital motivations in multimedia retrieval. The author has clearly established himself at the frontier of the the research field in multimedia retrieval."
Remco Veltkamp, U Utrecht, The Netherlands
"This book addresses content-based multimedia retrieval for time-dependent media. Focusing on two example applications (music and human motion data), Müller first gives good introductions into these areas, accompanied by many illustrative examples, and then presents the results of his own research. Overall, this volume is a good introduction into and survey of current research in the area of multimedia retrieval."
Norbert Fuhr, U Duisburg-Essen, Germany
"The work of Meinard Müller on synchronization of static and dynamic data-types in music and motion occupies a unique place in the rapidly growing literature of interdisciplinary studies concerned with computer techniques in the arts. The Bonn mathematics group from which it issues enjoys its own special distinction in the development of rigorous applications with wide applicability."
Eleanor Selfridge-Field, Stanford U, CA, USA
"Information Retrieval for Music and Motion is an outstanding contribution to the analysis of music, motion, and gesture. This collection of state-of-the-art techniques is an essential reference for researchers in computer graphics, computer vision, computer music, and multimedia."
Roger B. Dannenberg, School of Computer Science and School of Art, Carnegie Mellon University, Pittsburgh, PA, USA
"...this work is an extremely comprehensive and empirical look at music and motion retrieval" from the ACM Reviews by Quinsulon Israel, Drexel University, USA
