Efficient Multidimensional Searching Routines

The problem of Music Information Retrieval can often be formalized as “searching for multidimensional trajectories”. It is well known that string-matching techniques provide robust and effective theoretic solutions to this problem. However, for low dimensional searches, especially queries concerning a single vector as opposed to a series of vectors, there are a wide variety of other methods available. In this work we examine and benchmark those methods and attempt to determine if they may be useful in the field of information retrieval. Notably, we propose the use of KD-Trees for multidimensional nearneighbor searching. We show that a KD-Tree is optimized for multidimensional data, and is preferred over other methods that have been suggested, such as the K-Tree, the box-assisted sort and the multidimensional quick-sort.

• Type: Conference
• Year: 2001
• Where: ISMIR
• Authors: Josh Reiss
• Keywords: Information Retrieval, ISMIR 2001, Multidimensional, Multidimensional Trajectories, Music