Model based audio sequence alignment based on deterministic similarity methods

In this work, we proposed two new generative models for multiple audio sequence alignment based on correlation and Hamming distance. Here, we focused on pairwise alignment however the framework extends directly to multiple alignment cases. The simulation results on real data sets suggest that our method is very robust and efficient for overlapping or non-overlapping cases under very noisy conditions with proper choices of model parameters.