Proceedings of the Annual Meeting of the Cognitive Science Society

A critical aspect of the way humans recognize and understand meaning in sequential data is the ability to identify abstract structural repetitions. We present a novel approach to discovering structural repetitions within sequences that uses a multiple Smith-Waterman self-alignment. We illustrate our approach in the context of finding different forms of structural repetition in music composition. Feature-specific alignment scoring functions enable structure finding in primitive features such as rhythm, melody, and lyrics. These can be compounded to create scoring functions that find higher-level structure including verse-chorus structure. We demonstrate our approach by finding harmonic, pitch, rhythmic, and lyrical structure in symbolic music and compounding these viewpoints to identify the abstract structure of verse-chorus segmentation.