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- W2813308518 abstract "This thesis explores the challenge of automatic music transcription with a combination of digital signal processing and machine learning methods. Automatic music transcription is important for musicians who can't do it themselves or find it tedious. We start with an existing model, designed by Sigtia, Benetos and Dixon, and develop it in a number of original ways. We find that by using convolutional neural networks with filter shapes more tailored for spectrogram data, we see better and faster transcription results when evaluating the new model on a dataset of classical piano music. We also find that employing better practices shows improved results. Finally, we open-source our test bed for pre-processing, training, and testing the models to assist in future research." @default.
- W2813308518 created "2018-07-19" @default.
- W2813308518 creator A5087180786 @default.
- W2813308518 date "2019-12-04" @default.
- W2813308518 modified "2023-09-27" @default.
- W2813308518 title "Automatic Music Transcription with Convolutional Neural Networks using Intuitive Filter Shapes" @default.
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- W2813308518 doi "https://doi.org/10.15368/theses.2017.95" @default.
- W2813308518 hasPublicationYear "2019" @default.
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