Information Retrieval for Music and Motion [Paperback]

Content-based multimedia retrieval is a challenging research field with many unsolved problems. This monograph details concepts and algorithms for robust and efficient information retrieval of two different types of multimedia data: waveform-based music data and human motion data. It first examines several approaches in music information retrieval, in particular general strategies as well as efficient algorithms. The book then 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.

This monograph 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.

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 M?ller 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 lóÑ