Abstract
Traditional research into the arts has generally been based around the subjective judgment of human critics. We propose an alternative approach based on the use of objective machine learning programs. To illustrate this methodology we investigated the distribution of music from around the world: geographical ethnomusicology. To ensure that the knowledge obtained about geographical ethnomusicology is objective and operational we cast the problem as a machine learning one: predicting the geographical origin of pieces of music. We collected 1,142 pieces of music from 73 countries, and described them using 2 sets of standard audio descriptors using MARSYAS. To predict the location of origin of the music we developed a method designed to deal with the spherical surface topology based upon a modified k-nearest-neighbour. We also investigated the utility of a priori geographical knowledge in the predictions: a land and sea mask, and a population distribution overlay. The best-performing prediction method achieved a median land distance error of 1,506km, with comparable random trials having mean of medians 3,190km - this is significant at P < 0.001.
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Q, C., King, R.D. (2013). Retracted: Machine Learning as an Objective Approach to Understanding Music. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2012. Lecture Notes in Computer Science(), vol 7765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37382-4_5
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DOI: https://doi.org/10.1007/978-3-642-37382-4_5
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