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The rising availability of digital music stipulates effective categorizationand retrieval methods. Real world scenarios are characterized by mammoth musiccollections through pertinent and non-pertinent songs with reference to theuser input. The primary goal of the research work is to counter balance theperilous impact of non-relevant songs through Progressive Filtering (PF) forQuery by Humming (QBH) system. PF is a technique of problem solving throughreduced space. This paper presents the concept of PF and its efficient designbased on Multi-Resolution Histograms (MRH) to accomplish searching inmanifolds. Initially the entire music database is searched to obtain highrecall rate and narrowed search space. Later steps accomplish slow search inthe reduced periphery and achieve additional accuracy.  Experimentation on large music database using recursive programmingsubstantiates the potential of the method. The outcome of proposed strategyglimpses that MRH effectively locate the patterns. Distances of MRH at lowerlevel are the lower bounds of the distances at higher level, which guaranteesevasion of false dismissals during PF. In due course, proposed method helps tostrike a balance between efficiency and effectiveness. The system is scalablefor large music retrieval systems and also data driven for performanceoptimization as an added advantage.

Progressive Filtering Using Multiresolution Histograms for Query by Humming System