Dimitris Kamarotos

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This paper presents an efficient method for recognizing isolated musical patterns in a monophonic environment, using a novel extension of Dynamic Time Warping, which we call Context Dependent Dynamic Time Warping. Each pattern is converted into a sequence of frequency jumps by means of a fundamental frequency tracking algorithm, followed by a quantizer. The(More)
This paper presents a new extension to the variable duration hidden Markov model (HMM), capable of classifying musical pattens that have been extracted from raw audio data into a set of predefined classes. Each musical pattern is converted into a sequence of music intervals by means of a fundamental frequency tracking procedure. This sequence is(More)
Recognition of pre-defined musical patterns is very useful to researchers in Musicology and Ethnomusicology. This paper presents a novel efficient method for recognizing isolated musical patterns, using discrete observation Hidden Markov Models. The first stage of our method is to extract a vector of frequencies from the musical pattern to be recognized.(More)
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