Examining Synthetic Databases in Melodic Retrieval Testing
Charles Parker · 2004
We investigate the practice of using probabilistically generated melodies to do large scale evaluations of query-byhumming systems by running a set of sung queries against both real and synthetic databases, using an already verified type of “matching function ” to map sung queries to the appropriate target. We find that the accuracy of the generative process can be improved by introducing a first-order Markov assumption into the model, though neither method of melodic generation is found to be a statistically consistant approximation of an actual database under our experimental conditions. 1