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In this repository, the composer classification problem is described and a new network architecture based on a combination of ConvNets and GRUs is proposed to address the problem. Methods of data augmentation are described and a trend of increasing accuracy with augmentation factor is reported. The best test set accuracy obtained is 72.1%.
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In this repository, the composer classification problem is described and a new network architecture based on a combination of ConvNets and GRUs is proposed to address the problem. Methods of data augmentation are described and a trend of increasing accuracy with augmentation factor is reported. The best test set accuracy obtained is 72.1%.
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