OFAI, Vienna

The Austrian Research Institute for Artificial Intelligence (OFAI) is one of Europe's leading non-profit contract research institutions. It has been cooperating with international and national organisations, companies, universities and research institutes from 28 countries.

Services offered range from consultancy, basic and applied research on a contractual base, to working as partner or prime contractor in scientific projects on a national or European level, with a twenty year experience in a diversity of domains, from economic, engineering, administrative to social and cultural tasks.

The Intelligent Music Processing and Machine Learning Group (IMP/ML) of OFAI is a team of currently 12 members, dedicated to a variety of music and machine learning related topics:

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).
Maarten Grachten PI - Principal Investigator
Carlos Cancino Chacon Researcher
Stefan Lattner Researcher
Gerhard Widmer Scientific Advisor