Hierarchical Clustering of Music Database Based on HMM and Markov Chain for Search Efficiency

Music search unlike the regular text search works on huge databases and traditional pattern matching approaches are not feasible. The efficiency of a music search engine solely depends on the data categorization scheme employed. The proposed idea aims to reduce search complexity using tree based org...

Full description

Saved in:
Bibliographic Details
Published inSpeech, Sound and Music Processing: Embracing Research in India pp. 98 - 103
Main Authors Ross, Joe Cheri, Samuel, John
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
content based information retrieval
Hidden Markov Model
information retrieval
Markov Chain
Music
music search
Scale based search
Online AccessGet full text

Cover

More Information
Summary:Music search unlike the regular text search works on huge databases and traditional pattern matching approaches are not feasible. The efficiency of a music search engine solely depends on the data categorization scheme employed. The proposed idea aims to reduce search complexity using tree based organization of music database and also considering scale, chord and note transition of the input query. Probabilistic modeling of chord transition by Hidden Markov model and notes transition through Markov chain improvise on clustering enormous music data, eventually resulting in search complexity reduction. The method inherently supports minor deviations in the input query which may prevent meeting user expectations despite the availability of data.
ISBN:9783642319792
3642319793
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-31980-8_9