HOW TO BE LOST: PRINCIPLED PRIMING AND PRUNING WITH PARTICLES IN SCORE FOLLOWING
Charles Warren Fox, John A. Quinn · University of Michigan Library Repository · 2007
Previous work in score following has provided methods for aligning a skilled live performance to a symbolic or audio score.In the Bayesian framework, ideal generative models require O(n) computations at each real time step where n is the length of the score.In practice, heuristic thresholds have been used to consider only a subspace of generative models with high priors conditioned on the previous state.These heuristics work well for skilled performances but fail when large errors are made by amateur musicians.We present a novel Priming Particle Filter for audio scores which places the order-limiting heuristic on a firm foundation and adds the ability to recover from large errors by using psychologically-inspired bottom-up priming in addition to regular sequential importance sampling.