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Music identification is the process of matching an audio stream to a particular song. Previous work has relied on hashing, where an exact or almost-exact match between local features of the test and reference recordings is required. In this work we present a new approach to music identification based on finite-state transducers and Gaussian mixture models. We apply an unsupervised training process to learn an inventory of music phone units similar to phonemes in speech. We also learn a uniquedoi:10.1109/icassp.2007.366329 dblp:conf/icassp/WeinsteinM07 fatcat:tngqcbpyljbv3bzxwyk4f4aige