Music Genre Recognition
Our objective in this project was training a deep learning model to be able to recognize a song music genre. We used the GTZAN dataset to test our approach.
We adapt the model from Choi et al. to train a custom music genre classification system with our own genres and data. The model takes as an input the spectogram of music frames and analyzes the image using a Convolutional Neural Network (CNN) plus a Recurrent Neural Network (RNN). We compute the genre every 30 seconds of music and provide the average genre in the end. The output of the system is a vector of predicted genres for the song.
We fine-tune Choi's model with a small dataset (30 songs per genre) and test it on the GTZAN dataset achieving a final accuracy of 80%.