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This work addresses the problem of locating rhythmic patterns in music recordings. During the feature extraction stage, a short-term processing technique is applied, in order to detect significant changes in the spectral and energy evolution of the music signal. The detected changes are in turn treated as onsets of events and a sequence of inter-onset intervals is extracted. The resulting sequence is long-term segmented and is fed as input to a hidden Markov model (HMM) which models a predefined rhythmic pattern. An enhanced Viterbi algorithm is proposed, that extracts a best-state sequence, which determines the pattern location boundaries. Our method was tested on a set of music recordings of music meter 2/4, 3/4, 7/8 and 9/8 and steady tempo. The proposed method exhibits excellent precision (100%) over pattern locations and a recall ranging from ~ 34% up to ~ 74% depending on the music genre.