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Tutorial 1: Analysis and Retrieval Techniques for Music and Motion Data

Location: Room 102, TICC

Presented by

Meinard Mueller

Abstract

Modern information society is experiencing an explosion of digital content, comprising text, audio, video and graphics. The challenge is to organize, understand, and search multimodal information in a robust, efficient and intelligent manner. One challenge arises from the fact that 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. In this tutorial, we discuss fundamental algorithms and concepts for the analysis, classification, indexing, and retrieval of time-dependent data streams by means of two different types of multimedia data: music data and human motion data. In the music domain, we present techniques for automatic music alignment, synchronization, and matching. The common goal of these tasks is to automatically link several types of music representations (e.g., audio, MIDI, sheet music) thus coordinating the multiple information sources related to a given musical work. In the motion domain, we show how one can adopt standard indexing methods allowing for flexible and efficient content-based retrieval for large motion capture data sets by handling spatio-temporal motion deformations already on the feature level.

Speaker Biography

Meinard Mueller studied mathematics and computer science at Bonn University, Germany, where he received both a Master's degree in mathematics and his PhD in computer science in the year 1997 and 2001, respectively. In 2002/2003, he conducted postdoctoral research in combinatorics at the Mathematical Department of Keio University, Japan. In 2007, he obtained his Habilitation in the field of multimedia retrieval. His Habilitation thesis appeared as Springer monograph titled "Information Retrieval for Music and Motion" [5]. Currently, Meinard Mueller is a member of the Saarland University and the Max-Planck Institute for Informatics working as a senior researcher within the Excellence Cluster "Multimodal Computing and Interaction." His recent research interests include content-based multimedia retrieval, audio signal processing, computational musicology, and analysis of 3D motion capture data.