PhD Thesis: Indexing Content-Based Music Similarity Models for Fast Retrieval in Massive Databases

Indexing Content-Based Music Similarity Models for Fast Retrieval in Massive Databases

~ Dealing with the Music of the World ~
Download PhD Thesis
Defense Presentation (Jan. 31, 2012)

This thesis develops a large-scale music recommendation system. Three problems are solved preventing the currently top-performing class of content-based music similarity algorithms from being used as recommendation engine in huge databases with millions of songs.

  1. It is shown how to correctly use their non-vectorial music similarity features with their non-metric divergences in centroid-computing algorithms.
  2. An alleviation to the problem of “hubs” is presented.
  3. A method to speed up music recommendation queries is developed.

All three methods are merged in a large-scale, high-quality music recommendation prototype. The prototype is called “Wolperdinger” and operates on a collection of 2.3 million songs. A query is processed in a fraction of a second on a standard PC.

Errata

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Video of the Prototype

The video shows a music recommendation system operating on 2.3 million songs.

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