Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
A practical query-by-humming system for a large music database
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-based organization and visualization of music archives
Proceedings of the tenth ACM international conference on Multimedia
Music recommendation and query-by-content using self-organizing maps
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
A novel technique for data visualization based on SOM
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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Music consists of sequences, e.g., melodic, rhythmic or harmonic passages. The analysis and automatic discovery of sequences in music has an important part to play in different applications, e.g., intelligent fast-forward to new parts of a song, assisting tools in music composition, or automated spinning of records. In this paper we introduce a method for the automatic discovery of sequences in a song based on self-organizing maps and approximate motif search. In a preprocessing step high-dimensional music feature vectors are extracted on the level of bars, and translated into low-dimensional symbols, i.e., neurons of a self-organizing feature map. We use this quantization of bars for visualization of the song structure and for the recognition of motifs. An experimental analysis on real music data and a comparison to human analysis complements the results.