Gtzan.keras

gtzan.keras

Music Genre classification using Convolutional Neural Networks. Implemented in Tensorflow 2.0 using the Keras API

Overview

tl;dr: Compare the classic approach of extract features and use a classifier (e.g SVM) against the Deep Learning approach of using CNNs on a representation of the audio (Melspectrogram) to extract features and classify. You can see both approaches on the nbs folder in the Jupyter notebooks.

Resume of the deep learning approach:

1. Shuffle the input and split into train and test (70%/30%)
2. Read the audios as melspectrograms, spliting then into 1.5s windows with 50% overlaping resulting in a dataset with shape (samples x time x frequency x channels)
3. Train the CNN and test on test set using a Majority Voting approach

Results

To compare the result across multiple architectures, we have took two approaches for this problem: One using the classic approach of extracting features and then using a classifier. The second approach, wich is implemented on the src file here is a Deep Learning approach feeding a CNN with a melspectrogram.

You can check in the nbs folder on how we extracted the features, but here are the current results on the test set:

For the deep learning approach we have tested a simple custom architecture that can be found at the nbs folder.

Dataset

And how to get the dataset?

1. Download the GTZAN dataset here

Extract the file in the data folder of this project. The structure should look like this:

├── data/
   ├── genres
      ├── blues
      ├── classical
      ├── country
      .
      .
      .
      ├── rock

How to run

The models are provided as .joblib or .h5 files in the models folder. You just need to use it on your custom file as described bellow.

If you want to run the training process yourself, you need to run the provided notebooks in nbs folder.

To apply the model on a test file, you need to run:

$ cd src/
$ python app.py -t MODEL_TYPE -m ../models/PATH_TO_MODEL -s PATH_TO_SONG

Where MODEL_TYPE = [ml, dl] for classical machine learning approach and for a deep learning approach, respectively.

Usage example:

$ python app.py -t dl -m ../models/custom_cnn_2d.h5 -s ../data/samples/iza_meu_talisma.mp3

and the output will be:

$ ../data/samples/iza_meu_talisma.mp3 is a pop song
$ most likely genres are: [('pop', 0.43), ('hiphop', 0.39), ('country', 0.08)]