In-place versus re-build versus re-merge: Index maintenance strategies for text retrieval systems

Lester, N, Zobel, J and Williams, H 2004, 'In-place versus re-build versus re-merge: Index maintenance strategies for text retrieval systems', in Computer Science 2004 - Proceedings of the 27th Australasian Computer Science Conference, Dunedin, 18 January 2004, pp. 315-322.


Document type: Conference Paper
Collection: Conference Papers

Title In-place versus re-build versus re-merge: Index maintenance strategies for text retrieval systems
Author(s) Lester, N
Zobel, J
Williams, H
Year 2004
Conference name Australasian Computer Science Conference
Conference location Dunedin
Conference dates 18 January 2004
Proceedings title Computer Science 2004 - Proceedings of the 27th Australasian Computer Science Conference
Publisher Australian Computer Society
Place of publication Bedford Park, SA
Start page 315
End page 322
Total pages 8
Abstract Attempts to categorise music by extracting audio features from a sample have had mixed results. Some categories such as classical are easy to identify but attempts to distinguish between various types of popular music yield poor results. Part of the difficulty is that humans also disagree with each other when classifying music. We report on experiments that compare human classification of music samples to that based on audio feature extraction and machine learning techniques. We extracted a set of audio features and applied a range of machine learning techniques to a set of 128 pieces of music. Our work demonstrates that a single feature and a simple machine learning approach achieve results that are almost as consistent as humans for the same task. Further experiments revealed an even greater inconsistency amongst humans in selecting categories for music. Using a self-organising map on the same set of pieces and features produced some meaningful song clusters, that is, pieces by the same artist or composer, or of the same genre, were grouped together. It also showed some of the same cross-genre relationships shown by the human-based classifications
Subjects Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence)
Keyword(s) Information retrieval
Text retrieval systems
Indexes
Efficiency
Copyright notice © 2004 Australian Computer Society, Inc.
Versions
Version Filter Type
Access Statistics: 141 Abstract Views  -  Detailed Statistics
Created: Tue, 01 Sep 2009, 10:52:41 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us