Model based multiple audio sequence alignment

We formulate alignment of multiple and partially overlapping audio sequences in a probabilistic framework. We define and compare four generative models for time varying features extracted from audio clips that are recorded independently and asynchronously. We are able to handle missing data and multiple clips where no clip is covering the entire material. We define proper scoring functions for each model and the matching is achieved with a sequential alignment algorithm. The simulation results on real data suggest that the approach is able to handle difficult ambiguous scenarios or partial matchings.