Query by Humming: How good can it get?

When explaining the Query-by-humming (QBH) task, it is typical to describe it in terms of a musical question posed to a human expert, such as a music-store clerk. An evaluation of human performance on the task can shed light on how well one can reasonably expect an automated QBH system to perform. This paper describes a simple example experiment comparing three QBH systems to three human listeners. The systems compared depend on either a dynamic-programming implementation of probabilistic string matching, or hidden Markov models. While results are preliminary, they indicate existing string matching and Markov model performance does not currently achieve humanlevel performance.