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Structuring of Multimedia-Collections and Attribute Extraction

Recently, music became available in digital form via the internet. Due to the properties of audio data collections new methods of computer-aided retrieval are necessary. Music Information Retrieval (MIR) is a very active research area coping with the development of intelligent retrieval methods for digital audio data. Machine learning can make an important contribution in order to
  • allow the automatic classification of music stiles, artists, instruments,....
  • ease the development of intuitive interfaces for music search and presentation
  • allows the development of intelligent search engines which are able to suggest new and interesting pieces to users.

Projects

SFB 475 subproject A4
Vista-TV

Software

RapidMiner (YALE)
RapidMiner Value Series Plugin

Staff

Beckers, Tobias
Bockermann, Christian

Past Master Thesis

Publications

Mierswa/etal/2008a Mierswa, Ingo and Morik, Katharina and Wurst, Michael. Handling Local Patterns in Collaborative Structuring. In Masseglia, Florent and Poncelet, Pascal and Teisseire, Maguelonne (editors), Successes and New Directions in Data Mining, pages 167 -- 186, IGI Global, 2008.
Mierswa/etal/2008b Mierswa, Ingo and Morik, Katharina and Wurst, Michael. Collaborative Use of Features in a Distributed System for the Organization of Music Collections. In Shen and Shephard and Cui and Liu (editors), Intelligent Music Information Systems: Tools and Methodologies, pages 147--176, Igi Global Publishing, 2008.
Flasch/etal/2007b Flasch, Oliver and Kaspari, Andreas and Morik, Katharina and Wurst, Michael. Aspect-Based Tagging for Collaborative Media Organization. In Berendt, Bettina and Hotho, Andreas and Mladenic, Dunja and Semeraro, Giovanni (editors), From Web to Social Web: Discovering and Deploying User and Content Profiles, Vol. 4737, pages 122-141, Springer, 2007.
Mierswa/etal/2007b Mierswa, Ingo and Morik, Katharina and Wurst, Michael. Collaborative Use of Features in a Distributed System for the Organization of Music Collections. In Shen, Shepherd, Cui, and Liu (editors), Intelligent Music Information Systems: Tools and Methodologies, pages 147 - 175, Information Science Reference, 2007.
Moerchen/etal/2006a Morchen, Fabian and Mierswa, Ingo and Ultsch, Alfred. Understandable models of music collections based on exhaustive feature generation with temporal statistics. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.
Heinle/2005a Heinle, Eduard. Benutzergeleitetes Clustering von Musikdaten. Fachbereich Informatik, Universit\"at Dortmund, 2005.
Homburg/etal/2005a Homburg, Helge and Mierswa,Ingo and Moller, Bulent and Morik, Katharina and Wurst, Michael. A Benchmark Dataset for Audio Classification and Clustering. In Joshua D. Reiss and Geraint A. Wiggins (editors), Proc. of the International Symposium on Music Information Retrieval 2005, pages 528--531, London, UK, Queen Mary University, 2005.
Mierswa/Morik/2005a Mierswa, Ingo and Morik, Katharina. Automatic Feature Extraction for Classifying Audio Data. In Machine Learning Journal, Vol. 58, pages 127--149, 2005.
Mierswa/Morik/2005b Mierswa, Ingo and Morik, Katharina. Method trees: building blocks for self-organizable representations of value series: how to evolve representations for classifying audio data. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2005, Workshop on Self-Organization In Representations For Evolutionary Algorithms: Building complexity from simplicity, pages 293--300, New York, NY, USA, ACM, 2005.
Mierswa/Morik/2005c Mierswa, Ingo and Morik, Katharina. Evolutionäre Aufzucht von Methodenbäumen zur Merkmalsextraktion aus Audiodaten. In Informatik Spektrum, Themenheft Musik, Vol. 28, No. 5, pages 381--388, 2005.
Mierswa/Wurst/2005b Mierswa, Ingo and Wurst, Michael. Efficient Feature Construction by Meta Learning -- Guiding the Search in Meta Hypothesis Space. In Proc. of the International Conference on Machine Learning, Workshop on Meta Learning, 2005.
Mierswa/Wurst/2005c Mierswa, Ingo and Wurst, Michael. Efficient Case Based Feature Construction for Heterogeneous Learning Tasks. In Alipio Jorge and Luis Torgo and Pavel Brazdil and Rui Camacho and Joao Gama (editors), Proceedings of the European Conference on Machine Learning (ECML 2005), pages 641--648, Berlin, Springer, 2005.
Morik/2005a Morik, Katharina. Informatik Spektrum, Themenheft Musik. 2005.
Wurst/etal/2005a Wurst, Michael and Mierswa, Ingo and Morik, Katharina. Structuring Music Collections by Exploiting Peers' Processing. No. 43/05, Collaborative Research Center 475, University of Dortmund, 2005.
Mierswa/2004a Mierswa, Ingo. Automatisierte Merkmalsextraktion aus Audiodaten. Fachbereich Informatik, Universit\"at Dortmund, 2004.
Mierswa/2004b Mierswa, Ingo. Automatic Feature Extraction from Large Time Series. In Weihs, C. and Gaul, W. (editors), Classification -- the Ubiquitous Challenge, Proc. of the 28. Annual Conference of the GfKl 2004, pages 600--607, Springer, 2004.
Mierswa/2004c Mierswa, Ingo. Automatic Feature Extraction from Large Time Series. In Abecker, A. and Bickel, S. and Brefeld, U. and Drost, I. and Henze, N. and Herden, O. and Minor, M. and Scheffer, T. and Stojanovic, L. and Weibelzahl, S. (editors), Proc. of LWA 2004 - Lernen - Wissensentdeckung - Adaptivitat, 2004.
Mierswa/Morik/2004a Mierswa, Ingo and Morik, Katharina. Learning Feature Extraction for Learning from Audio Data. No. 55/04, Collaborative Research Center 475, University of Dortmund, 2004.
Mierswa/2003a Mierswa, Ingo. Beatles vs. Bach: Merkmalsextraktion im Phasenraum von Audiodaten. In LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitat, 2003.