Polyphonic Music Classification on Symbolic Data Using Dissimilarity Functions
نویسندگان
چکیده
This paper addresses the polyphonic music classification problem on symbolic data. A new method is proposed which converts music pieces into binary chroma vector sequences and then classifies them by applying the dissimilaritybased classification method TWIST proposed in our previous work. One advantage of using TWIST is that it works with any dissimilarity measure. Computational experiments show that the proposed method drastically outperforms SVM and k-NN, the state-of-the-art classification methods.
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