Communications of the ACM
sing Machine Learning for Music Knowledge Discovery
Researchers tested natural language processing approaches that could help scientists uncover new hypotheses and identify interesting patterns in archived historical music-related documents.
Credit: Sergio Oramas et al.
Researchers from Spain’s University of Pompeu Fabra and the Technical University of Madrid, along with colleagues from Cardiff University in the U.K., collaborated on the use of machine learning algorithms to gain new insights about the history of music.
The researchers tested natural language processing (NLP) approaches that could help scientists uncover new hypotheses and identify interesting patterns in archived historical documents.
The team applied automatic linguistic processing to large collections of music-related texts. Their study relied on data from a variety of sources, including Wikipedia, DBpedia, and MusicBrainz, focusing on flamenco, Renaissance music, and popular music.
Using NLP, the researchers "extracted directly from the data which are the most influential flamenco and Renaissance artists, and discovered migratory tendencies of composers between European cities in the 15th and 16th century," explained Pompeu Fabra's Sergio Oramas.
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