The quantization problem: traditional and connectionist approaches
Understanding music with AI
Informedia Digital Video Library
Communications of the ACM
Towards the digital music library: tune retrieval from acoustic input
Proceedings of the first ACM international conference on Digital libraries
VIS '97 Proceedings of the 8th conference on Visualization '97
Indexing Multilingual Information on the Web
COMPSAC '98 Proceedings of the 22nd International Computer Software and Applications Conference
Selection of Melody Lines for Music Databases
COMPSAC '00 24th International Computer Software and Applications Conference
A System for Music Information Retrieval
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
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The design of content-based music retrieval systems on the Web is a challenge, since music is auditory, temporal, and multidimensional - the same piece can be interpreted in multiple ways. Most literatures on music retrieval simply map the problem to existing information retrieval paradigms, mainly that of text, by modeling music as a sequence of features. However, this mapping raises questions to be answered. Through the study of the statistical properties of six features, namely Profile, Note Duration Ratio Sequence, Interval Sequence and their variants, we answer four of these questions in this paper. They are: the number of musical "alphabets" and "words" in musical features, whether Zipf's law holds for musical features, whether there are any musical "stopwords", and the range of n for n-gram based music indices.