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.
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