The rising availability of digital music stipulates effective categorization
and retrieval methods. Real world scenarios are characterized by mammoth music
collections through pertinent and non-pertinent songs with reference to the
user input. The primary goal of the research work is to counter balance the
perilous impact of non-relevant songs through Progressive Filtering (PF) for
Query by Humming (QBH) system. PF is a technique of problem solving through
reduced space. This paper presents the concept of PF and its efficient design
based on Multi-Resolution Histograms (MRH) to accomplish searching in
manifolds. Initially the entire music database is searched to obtain high
recall rate and narrowed search space. Later steps accomplish slow search in
the reduced periphery and achieve additional accuracy.
Experimentation on large music database using recursive programming
substantiates the potential of the method. The outcome of proposed strategy
glimpses that MRH effectively locate the patterns. Distances of MRH at lower
level are the lower bounds of the distances at higher level, which guarantees
evasion of false dismissals during PF. In due course, proposed method helps to
strike a balance between efficiency and effectiveness. The system is scalable
for large music retrieval systems and also data driven for performance
optimization as an added advantage.Comment: 12 Pages, 6 Figures, Full version of the paper published at
ICMCCA-2012 with the same title,
Link:http://link.springer.com/chapter/10.1007/978-81-322-1143-3_2