Research Areas

Intelligent Music Information Retrieval

This recently established research field is of growing interest for both the research community and "normal" music consumers. Our work in MIR includes

Representation and Estimation of Musical Similarity
Organization and Visualization of Digital Music Archives
Genre Classification (from audio and/or web-based data)
Audio Alignment (semi-automatic indexing and generation of content-based metadata)
Detection and Classification of Rhythm

Machine Learning and Data Mining

Past and current research areas:

Data Mining and Knowledge Discovery in Databases
Text Mining
Metalearning and Evaluation of Learning Algorithms
Learning with Multiple Models
Inductive Logic Programming
Knowledge Intensive Learning
Concept Drift and Context-Sensitive Learning
Minimum Description Length Principle

Music Expression and Performance Research with Artificial Intelligence Methods

This area of research covers a vast variety of different subtopics and research tasks including

Data Acquisition (Score extraction from expressive MIDI files, score-to-performance matching, beat and tempo tracking in MIDI files, Beat and tempo tracking in audio data),
Piano acoustic studies (Analysis of the timing properties of piano actions, quality assessment of reproducing pianos such as the Bösendorfer SE system or the Yamaha Disklavier),
Automated Structural Music Analysis (Segmentation and Clustering and Motivic Analysis),
Tempo and Timing Perception (Perception of tempo, Perception of note onset asynchronies -- "melody lead," and Similarity perception of expressive performances),
Systematic Expressive Performance Analysis (Analysis of individual performance aspects as Articulation, Note onset asynchronies, Segmentation-timing relations),
Performance Visualization (animated two-dimensional tempo-loudness trajectories and real-time systems -- the "Performance Worm"),
Inductive Model Building -- Machine Learning (Fitting existing expression models onto real performance data, Looking for structure in extensive performance data, Inducing partial models of note-level expression principles, and Inducing multi-level models of phrase-level and note-level performance), and
Characterization and Automatic Classification of Great Artists (Learning to recognize performers from characteristics of their style, Discovering performance patterns characteristic of famous performers).