Meinard Müller details concepts and algorithms for robust and efficient information retrieval by means of two different types of multimedia data: waveform-based music data and human motion data. In Part I, he discusses in depth several approaches in music information retrieval, in particular general strategies as well as efficient algorithms for music synchronization, audio matching, and audio structure analysis. He also shows how the analysis results can be used in an advanced audio player to facilitate additional retrieval and browsing functionality. In Part II, he introduces a general and unified framework for motion analysis, retrieval, and classification, highlighting the design of suitable features, the notion of similarity used to compare data streams, and data organization. The detailed chapters at the beginning of each part give consideration to the interdisciplinary character of this field, covering information science, digital signal processing, audio engineering, musicology, and computer graphics.

This first monograph specializing in music and motion retrieval appeals to a wide audience, from students at the graduate level and lecturers to scientists working in the above mentioned fields in academia or industry. Lecturers and students will benefit from the didactic style, and each unit is suitable for stand-alone use in specialized graduate courses. Researchers will be interested in the detailed description of original research results and their application in real-world browsing and retrieval scenarios.

Meinard Müller is a Member of the Multimedia Signal Processing Group, Bonn University, working as a Researcher and Assistant Lecturer. His research interests include digital signal processing, multimedia information retrieval, computational group theory, and combinatorics. His special research topics include audio signal processing, computational musicology, analysis of 3D motion capture data, and content-based retrieval in multimedia documents.