audio_DCASE

Paper Implementation for :

[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]

This code runs on the DCASE 2016 Audio Dataset.

You need to define:

wav_dev_fd development audio folder

wav_eva_fd evaluation audio folder

dev_fd development features folder

eva_fd evaluation features folder

label_csv development meta file

txt_eva_path evaluation test file

new_p evaluation evaluate file

Cloning the repo

Go ahead and clone this repository using

$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run

If you are looking for a quick running version go inside single_file folder and run

$ python mainfile.py

Detailed Task

The process involves three steps:

1. Feature Extraction
2. Training on Development Dataset
3. Testing on Evaluation Dataset

Feature Extraction

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.

Training

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.