Audio classification model which uses CNN to train ESC-50 dataset.
git clone https://github.com/sweat0198/audio_classication_CNN_ESC-50/tree/master
matplotlib>=2.0.0
pandas>=0.21.1
Keras>=2.1.3
tensorflow>=1.5.0
scipy>=1.0.0
librosa>=0.5.1
scikit_learn>=0.19.1
IPython>=7.8.0
tqdm>=4.48.0
jupyter
notebook
There are simply a jupyter notebook and codes are explained there.
- Kaggle-notebook - Kaggle notebook used run notebook
- Pycharm - Pycharm is used for working on local machine
- Data extended to 5 times with autmentation functions after splitting data as %70 training, %20 validating and %10 testing.
- Model reached to 0.850 accuracy with 0.640 loss in 46 epochs which has 32 batch size.
- Testing made with unseen data by the model, so 0.85 accuracy satisfies the expectations.
Git Repositories | Comments |
---|---|
karolpiczak/ESC-50 | Dataset of 50 classes which each has 40 audio file |
shibuiwilliam/audio_classification_keras | Well designed notebook, best accuracy with unseen test files. This is the choosen one. |
micah5/pyAudioClassification | Dead Simple Usage, there exist svm, conv1d and conv2d algorithms. To who just want to classify some audio quickly. |
seth814/Audio-Classification | With youtube tutorial, it uses Conv1d, Conv2D and LSTM. Could not tested since it cuts audio files. |
drscotthawley/panotti | Favorite repo because it has a lot useful tools, but accuracy up to 55 with ESC-50 |
bheemnitd/Environmental-Sound-Classification | Could not evaluate correctly since it has mixed augmented data in test and train |
mtobeiyf/audio-classification | It has also svm, basic neural network and conv1d but accuracies of them are not high enough. Their max accuracy was 0.512 |
adanRivas/CNN-Audio-Classifier-with-Keras-Tensorflow | 65 accuracy with VGG16, pure data, may be it can be retrain with augmented data |
- https://github.com/shibuiwilliam/audio_classification_keras codes are taken from this repo and some modifications made on it.
- https://github.com/adanRivas/CNN-Audio-Classifier-with-Keras-Tensorflow
- https://github.com/drscotthawley/panotti
- Make the prediction flexible in time.