Learning and Visualizing Music Specifications Using Pattern Graphs

TitleLearning and Visualizing Music Specifications Using Pattern Graphs
Publication TypeConference Paper
Year of Publication2016
AuthorsValle, Rafael, J. Fremont Daniel, Akkaya Ilge, Donze Alexandre, Freed Adrian, and S. Seshia Sanjit
Refereed DesignationRefereed
Conference NameInternational Society for Music Information Retrieval Conference
Date Published2016
Conference LocationNew York
AbstractWe 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.
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