@CONFERENCE\{IMM2007-05355, author = "J. Arenas-García and A. Meng and K. B. Petersen and T. L. Schi{\o}ler and L. K. Hansen and J. Larsen", title = "Unveiling Music Structure Via {PLSA} Similarity Fusion", year = "2007", month = "aug", keywords = "{NMF,} {PLSA,} Combining Music Similarity", pages = "419-424", booktitle = "{IEEE} International Workshop on {IEEE} International Workshop on Machine Learning for Signal Processing", volume = "", series = "", editor = "Konstantinos Diamantaras, Tülay Adali, Ioannis Pitas, Jan Larsen, Theophilos Papadimitriou, Scott Douglas", publisher = "{IEEE} Press", organization = "", address = "", url = "http://www2.compute.dtu.dk/pubdb/pubs/5355-full.html", abstract = "Nowadays there is an increasing interest in developing methods for building music recommendation systems. In order to get a satis- factory performance from such a system, one needs to incorporate as much information about songs similarity as possible; however, how to do so is not obvious. In this paper, we build on the ideas of the Probabilistic Latent Semantic Analysis (PLSA) that has been successfully used in the document retrieval community. Under this probabilistic framework, any song will be projected into a rela- tively low dimensional space of {''}latent semantics{'',} in such a way that that all observed similarities can be satisfactorily explained us- ing the latent semantics. Additionally, this approach significantly simplifies the song retrieval phase, leading to a more practical sys- tem implementation. The suitability of the {PLSA} model for rep- resenting music structure is studied in a simplified scenario con- sisting of 10.000 songs and two similarity measures among them. The results suggest that the {PLSA} model is a useful framework to combine different sources of information, and provides a reason- able space for song representation.", isbn_issn = "ISBN: {1-}4244-1566-7" }