@conference {403,
	title = {Learning and Visualizing Music Specifications Using Pattern Graphs},
	booktitle = {International Society for Music Information Retrieval Conference},
	year = {2016},
	month = {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 procedure called specification mining. Our procedure extracts patterns from feature vectors and uses them to build pattern 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 pattern 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 results in song summarization, song and style validation and machine improvisation with formal specifications.},
	url = {https://18798-presscdn-pagely.netdna-ssl.com/ismir2016/wp-content/uploads/sites/2294/2016/07/280_Paper.pdf},
	attachments = {http://www.adrianfreed.com/sites/default/files/280_Paper.pdf},
	author = {Valle, Rafael and J. Fremont, Daniel and Akkaya, Ilge and Donze, Alexandre and Freed, Adrian and S. Seshia, Sanjit}
}
