SMC samplers for multiresolution audio sequence alignment
Doğaç Başaran, Ali Taylan Cemgil, Emin Anarım · 2013
In our previous work, we formulated multiple audio sequence alignment in a probabilistic framework [1]. Here, we extend the model for multi resolution alignment and focus on pairwise cases. We defined a similarity based approach for binary feature sequences and integrate it into a new generative model. We modify themodel formulti resolution case and the matching is achieved with a SequentialMonte Carlo Sampler (SMCS) which uses low resolution models as bridge distributions. The simulation results on real data sets suggest that our method is very robust and efficient under very noisy conditions with proper choices of model parameters.