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Music Information Retrieval on the Free Music Archive

Chibueze Ukachi, Michaël Defferrard

The goal of this semester project is to provide baselines for genre classification, and maybe other problems in Music Information Retrieval (MIR) if time allows, on the FMA dataset. We will first test simple ML algorithms then devise and test Deep Networks. The idea is to show that the dataset is large enough for end-to-end learning, i.e., from the raw audio, with current DL techniques. This task involves the use of Python, the Jupyter notebook and libraries such as scikit-learn and Keras (TensorFlow backend).

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