Using Fuzzy Song Sets in Music Warehouses

Using Fuzzy Song Sets in Music Warehouses

François Deliège (Aalborg University, Denmark) and Torben Bach Pedersen (Aalborg University, Denmark)
DOI: 10.4018/978-1-60566-858-1.ch003
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The emergence of music recommendation systems calls for the development of new data management technologies able to query vast music collections. In this chapter, the authors present a music warehouse prototype able to perform efficient nearest neighbor searches in an arbitrary song similarity space. Using fuzzy songs sets, the music warehouse offers a practical solution to three concrete musical data management scenarios: user musical preferences, user feedback, and song similarities. The authors investigate three practical approaches to tackle the storage issues of fuzzy song sets: tables, arrays, and compressed bitmaps. They confront theoretical estimates with practical implementation results and prove that, from a storage point of view, arrays and compressed bitmaps are both effective data structure solutions. With respect to speed, the authors show that operations on compressed bitmap offer a significant grain in performances for fuzzy song sets comprising a large number of songs. Finally, the authors argue that the presented results are not limited to music recommendations system but can be applied to other domains.
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Research on music recommendation systems has received a lot of attention lately. Current trends on playlist generation are focused on how to improve recommendations based on user-specific constrains. For example, a playlist generator that learns music preferences by taking user feedback into account was presented by Pauws & Eggen (2001). Other new interesting approaches concentrate on aggregating different music features; for instance, Bosteels & Keere (2007) study the use of generalized conjunctions and disjunctions of fuzzy sets theory for combining audio similarity measures. However, fewer researchers have addressed the scalability issues raised by these methods in terms of storage and performance (Aucouturier & Pachet, 2002; Pampalk, 2005). This chapter focuses specifically on the storage and performance issues and proposes to manipulate a large collection of musical data where song similarities, user preferences and user feedbacks are represented with fuzzy sets.

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