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Music genre recognition is a very interesting area of research in the broad scope of music information retrieval and audio signal processing. In this work we propose a novel approach for music genre recognition using an ensemble of convolutional long short term memory based neural networks (CNN LSTM) and a transfer learning model. The neural network models are trained on a diverse set of spectral and rhythmic features whereas the transfer learning model was originally trained on the task of music tagging. We compare our system with a number of recently published works and show that our model outperforms them and achieves new state of the art results.

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