Now showing items 1-3 of 3

    • The dangers of parsimony in query-by-humming applications 

      Colin Meek; William Birmingham (Johns Hopkins University, 2003-10-26)
      Query-by-humming systems attempt to address the needs of the non-expert user, for whom the most natural query format -- for the purposes of finding a tune, hook or melody of unknown providence -- is to sing it. While human ...
    • Effectiveness of HMM-Based Retrieval on Large Databases 

      Jonah Shifrin; William Birmingham (Johns Hopkins University, 2003-10-26)
      We have investigated the performance of a hidden Markov model based QBH retrieval system on a large musical database. The database is synthetic, generated from statistics gleaned from our (smaller) database of musical ...
    • The MUSART Testbed for Query-By-Humming Evaluation 

      Roger Dannenberg; William Birmingham; George Tzanetakis; Colin Meek; Ning Hu; Bryan Pardo (Johns Hopkins University, 2003-10-26)
      Evaluating music information retrieval systems is acknowledged to be a difficult problem. We have created a database and a software testbed for the systematic evaluation of various query-by-humming (QBH) search systems. ...