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In this thesis, we devise computational models for tracking sung lyrics in multi-instrumental music recordings. We consider not only the low-level acoustic characteristics, representing the timbre of the sung phonemes, but also higher-level music knowledge, that is complementary to lyrics. We build probabilistic models, based on dynamic Bayesian networks (DBN) that represent the relation of phoneme transitions to two music knowledge facets: the temporal structure of a lyrics line and thedoi:10.5281/zenodo.841980 fatcat:tohf6dcvobhe3ei77nvp3wg3ba