Error correction and accuracy scaling for a joint subtraction multiple fundamental frequency estimation algorithm

Abstract

This talk presents an improved multiple fundamental frequency (F0) estimation algorithm, which is loosely based on a previously developed joint subtraction algorithm [A. Klapuri, Proc. 7th Int. Conf. on Music Information Retrieval, 216–221 (2006)]. The improved algorithm uses some auditory scene analysis techniques to supplement a maximum likelihood based F0 estimator to predict the best combination of F0s. The accuracy of this base algorithm is then improved by use of error detection/correction. Error rates of the algorithm were collected using random mixes of sounds gathered from the University of Iowa Theremin database of musical instrument samples, as well as mixes of continuous audio. The performance of both the base algorithm and the improved algorithm will be presented together. It was found that the improved algorithm decreases error rates by as much as 50%, compared to the base algorithm with minimal loss in computational efficiency. The trade-offs between computational efficiency and accuracy, tested via a tunable parameter built into the algorithm, will be discussed. Trade-offs between computational costs and accuracy as a function of the number of F0s will also be discussed.