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.
DOI: 10.21437/Interspeech.2018-2045
Cite as: Ghosal, D., Kolekar, M.H. (2018) Music Genre Recognition Using Deep Neural Networks and Transfer Learning. Proc. Interspeech 2018, 2087-2091, DOI: 10.21437/Interspeech.2018-2045.
@inproceedings{Ghosal2018,
author={Deepanway Ghosal and Maheshkumar H. Kolekar},
title={Music Genre Recognition Using Deep Neural Networks and Transfer Learning},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={2087--2091},
doi={10.21437/Interspeech.2018-2045},
url={http://dx.doi.org/10.21437/Interspeech.2018-2045}
}