Abstract
We describe the MusicMiner system for organizing large collections of music with databionic mining techniques. Visualization based on perceptually motivated audio features and Emergent Self-Organizing Maps enables the unsupervised discovery of timbrally consistent clusters that may or may not correspond to musical genres and artists. We demonstrate the visualization capabilities of the U-Map. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The user can navigate the sound space and interact with the maps to play music or show the context of a song.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
AUCOUTURIER, J.-J. and PACHET F. (2002): Finding songs that sound the same. In Proc. of IEEE Benelux Workshop on Model based Processing and Coding of Audio, 1–8.
AUCOUTURIER, J.-J. and PACHET F. (2003): Representing musical genre: a state of art. JNMR, 31(1), 1–8.
LI, D., SETHI, I.K., DIMITROVA, N., and MCGEE, T. (2001): Classification of general audio data for content-based retrieval. Pattern Recognition Letters, 22, 533–544.
LOGAN, B. and SALOMON, A. (2001): A music similarity function based on signal analysis. In IEEE Intl. Conf. on Multimedia and Expo, 190–194.
MCKINNEY, M.F. and BREEBART, J. (2003): Features for audio and music classification. In Proc. ISMIR, 151–158.
MIERSWA, I. and MORIK, K. (2005): Automatic feature extraction for classifying audio data. Machine Learning Journal, 58:0, 127–149.
MÖRCHEN, F., ULTSCH, A., THIES, M., LÖHKEN, I., NÖCKER, M., STAMM, C., EFTHYMIOU, N., and KÜMMERER, M. (2005): MusicMiner: Visualizing perceptual distances of music as topograpical maps. Technical Report 47, CS Department, University Marburg, Germany.
PAMPALK, E., DIXON, S., and WIDMER, G. (2003): On the evaluation of perceptual similarity measures for music. In Intl. Conf. on Digital Audio Effects (DAFx), 6–12.
PAMPALK, E., RAUBER, A., and MERKL, D. (2002): Content-based organization and visualization of music archives. In Proc. of the ACM Multimedia, 570–579.
TORRENS, M., HERTZOG, P., and ARCOS, J.L. (2004): Visualizing and exploring personal music libraries. In Proc. ISMIR.
TZANETAKIS, G. and COOK, P. (2002): Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5).
TZANETAKIS, G., ERMOLINSKYI, A., and COOK, P. (2002): Beyond the queryby-example paradigm: New query interfaces for music. In Proc. ICMC.
ULTSCH, A. (1992): Self-organizing neural networks for visualization and classification. In Proc. GfKl, Dortmund, Germany.
ULTSCH, A. (2003): Pareto Density Estimation: Probability Density Estimation for Knowledge Discovery. In Proc. GfKl, Cottbus, Germany, 91–102.
ULTSCH, A. and MÖRCHEN, F. (2005): ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM. Technical Report 46, CS Department, University Marburg, Germany.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Berlin · Heidelberg
About this paper
Cite this paper
Mörchen, F., Ultsch, A., Nöcker, M., Stamm, C. (2006). Visual Mining in Music Collections. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_89
Download citation
DOI: https://doi.org/10.1007/3-540-31314-1_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
Online ISBN: 978-3-540-31314-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)