Paper Info
Title
HMM-based musical query retrieval
Abstract
We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. Pieces in the database are represented as hidden Markov models (HMMs). The query is treated as an observation sequence and a piece is judged similar to the query if its HMM has a high likelihood of generating the query. The top pieces are returned to the user in rank-order. This paper reports the basic approach for the construction of the target database of themes, encoding and transcription of user queries, and the results of initial experimentation with a small set of sung queries.
Year
DOI
Venue
2002
10.1145/544220.544291
JCDL
Keywords
Field
DocType
music search,target database,small set,hidden markov model,hmm-based musical query retrieval,high likelihood,basic approach,user query,top piece,observation sequence,initial experimentation,forward algorithm,melody,music,database
Query optimization,Web search query,Query language,Query expansion,Forward algorithm,Information retrieval,Computer science,Web query classification,View,Hidden Markov model
Conference
ISBN
Citations 
PageRank 
1-58113-513-0
37
2.32
References 
Authors
6
4
Name
Order
Citations
PageRank
Jonah Shifrin11137.82
Bryan Pardo283063.92
Colin Meek324725.54
William Birmingham4886.20