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Neural Information ProcessingMachine Learning and Neural Networks for the Perceptually Relevant Analysis of Music

Neuronale Informationsverarbeitung

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Machine Learning and Neural Networks for the Perceptually Relevant Analysis of Music

Lupe

Music contains structural information as well as semantic connotations which are easy to perceive by human listeners, but which are difficult to extract automatically from an acoustic event (and even from the score of a given piece of music). Here we explore new techniques from the machine learning and the mathematical music theory fields with the goal to create semantically meaningful representations from acoustic events and to automatically extract perceptually relevant patterns from music and sound.

Acknowledgement: Research was funded by the EU and by the Technische Universität Berlin.

Selected Publications:

Correspondence Analysis of Pitch Class, Key, and Composer
Citation key Purwins2004
Author Purwins, H. and Graepel, T. and Obermayer, K.
Title of Book Perspectives of Mathematical and Computational Music Theory
Pages 432 – 454
Year 2004
Editor Mazzola, G. and Noll, T. and Luis-Puebla, E.
Publisher Epos-Verlag
Bibtex Type of Publication Selected:music
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