A general scenario that has attracted a lot of attention for multimedia information retrieval is based on the query-by-example paradigm: retrieve all documents from a database containing parts or aspects similar to a given data fragment. However, multimedia objects, even though they are similar from a structural or semantic viewpoint, often reveal significant spatial or temporal differences. This makes content-based multimedia retrieval a challenging research field with many unsolved problems. Meinard Mller 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 advancedaudio 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.