@conference {406,
	title = {Learning and Visualizing Music Specifications Using Pattern Graphs},
	booktitle = {ISMIR 2016},
	year = {2016},
	address = {New York},
	abstract = {We describe a system to learn and visualize specifications from song(s) in symbolic and audio formats. The core of our approach is based on a software engineering proce-dure called specification mining. Our procedure extracts patterns from feature vectors and uses them to build pat-tern graphs. The feature vectors are created by segmenting song(s) and extracting time and and frequency domain features from them, such as chromagrams, chord degree and interval classification. The pattern graphs built on these feature vectors provide the likelihood of a pattern between nodes, as well as start and ending nodes. The pat-tern graphs learned from a song(s) describe formal specifications that can be used for human interpretable quantitatively and qualitatively song comparison or to perform supervisory control in machine improvisation. We offer r-sults in song summarization, song and style validation and machine improvisation with formal specifications.},
	attachments = {http://www.adrianfreed.com/sites/default/files/280_Paper_0.pdf},
	author = {Valle, Rafael and Fremont, Daniel J. and Akkaya, Ilge and Donz{\'e}, Alexandre and Freed, Adrian and Seshia, Sanjit A.}
}
