MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
The Geodesic Self-Organizing Map and its error analysis
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
A music search engine built upon audio-based and web-based similarity measures
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Multimodal Music Mood Classification Using Audio and Lyrics
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Combining audio content and social context for semantic music discovery
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Cultural style based music classification of audio signals
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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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