Efficient implementation of probabilistic multi-pitch tracking

We significantly improve the computational efficiency of a probabilistic approach for multiple pitch tracking. This method is based on a factorial hidden Markov model and two alternative interaction models for magnitude and log-magnitude spectra, respectively. The main computational bottleneck comprises the determination of observation likelihoods. However, we show that up to 99.5% of the smallest likelihood values can be discarded at each time frame with out affecting the overall tracking accuracy. For both interaction models, we present a heuristic to efficiently find the largest likelihood values. Experiments on the GRID database show that the proposed methods result in a major speedup without significantly changing tracking accuracy.