An attempt at genre classification with convolutional neural networks and spectrograms
Switch branches/tags
Nothing to show
Clone or download
Latest commit 2d1ca44 Nov 25, 2017

Neural Music

This is a project called neuralMusic created by Clayton Blythe on 2017/09/29

I aim to use the Free Music Archive (FMA) along with Convolutional Neural Networks in PyTorch to do genre classification from short snippets of different songs (~6 seconds of audio). I am working on taking random 6 second samples from 100,000 songs to train a classifier.

Here is an example of what a spectrogram looks like, it is kind of like a "fingerprint" for a song, representing how different frequencies of sound evolve over time.

For example, here is a six second snippet from "Lose Yourself To Dance" by Daft Punk

Alt Test

Convolutional Neural Networks have contributed to amazing advancements in image recognition, and this dataset is fairly large, so I am looking to see how good they are at converting the visual representation of a snippet of audio into genre predictions. I imagine it could be a cool app where you can get classification of a genre from a very short recording of audio.

With an initial test on 5.94 second length samples, training on ~5500 samples and testing on ~2200 validation examples, I achieved an accuracy of ~50% at classifying a song's membership to one of eight genre classses. I think these results are pretty decent considering the small size of data I used, and for such a short time snippet (5.94s). I uses a model of five hidden layers inspired by the vgg net, employing convolutions, batch normalizations and ReLU at each layer.

I plan on continuing this project, looking at larger amounts of training examples and incorporating data augmentation as well as different types of model architectures like squeeze net.