Environmental Sound Classification
Author: Bhavika Tekwani
- constants.py: Contains various settings for our CNNs & the filepaths for the dataset & pickled numpy arrays.
- create_rep.py: Creates the 2-channel representation of the audio signals. Generates 4 NumPy arrays and stores them
in the output folder as pickled
.npyobjects. These pickles represent the train features, train labels, test features & test labels.
- explore.py: Generates samples and visualizes them using a visualizer in librosa. These are utility functions which aided the creation of graphs & statistics for the report. No pseudocode provided for this file.
- cnn.py: Contains the Tensorflow model definition of the 1 layer CNN (referred to as CNN-1 from now on).
- sbcnn.py: Contains the Keras model for the Salamon & Bello CNN architecture.
- output/results.txt: Contains a summary of results which are used in the slides & report.
To install all the required modules to run the 5 files listed above, run the following line within the UrbanSound folder. You must have Python 3 installed. By default, we are using Tensorflow & Keras without GPU support.
pip install -r requirements.txt
- pseudocode: This folder contains the pseudocode for each of the above source files with the same name.
- Environmental Sound Classification.pdf: Summary of idea, methods, results & references.
- UrbanSound8K.pdf: Slides.
- output: This folder will contain 4 .npy files after create_rep.py has run.