Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py
The process involves three steps:
- Feature Extraction
- Training on Development Dataset
- Testing on Evaluation Dataset
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py
This will run the model which we test and use EER for rating our model.