Timbre-based Drum Pattern Classification using Hidden Markov Models
نویسنده
چکیده
In order to explore the possibility of a timbre-based rhythm theory, a drum pattern classification system was developed, which is capable of describing the internal structure of a drum groove in a stochastic way. Using an onset detection algorithm, timbral features were extracted at every drum onset of the sample file. Next, a Hidden Markov Model (HMM) was fitted to the data. Local decoding of the model showed that the rate of correct classifications lies at 100 % when examining plain samples and decreases with advancing musical complexity. Furthermore, similar sounds were decoded di↵erently.
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