University of Twente Student Theses
Classifying Classical Piano Music Into Time Period Using Machine Learning
Spijker, B.C. van 't (2020) Classifying Classical Piano Music Into Time Period Using Machine Learning.
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| Abstract: | The combination of Music and Machine Learning is a popular topic. Much research has been done attempting to classify pop music in different genres, but classical music has been looked at less, mainly because of the lack of available datasets. In this paper, classical piano music dating from the 17th to the 20th century is classified into the category period of composition. For this the MAESTRO dataset from Magenta is used, consisting of approximately 200 hours of piano performance recordings. Mel-Frequency Cepstral Coefficients and Chromagrams are evaluated as features, and they are tested with Convolutional Neural Networks, Long Short-Term Memory (a type of Recurrent Neural Networks) and Support Vector Machines as Machine Learning algorithms. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 01 general works, 02 science and culture in general, 24 dramaturgy, musicology, 54 computer science |
| Programme: | Business Information Technology MSc (60025) |
| Link to this item: | https://purl.utwente.nl/essays/80618 |
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