Using 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. 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(NLP) approaches that could help scientists uncover new hypotheses and identify interesting patterns in archived historical documents.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.