https://github.com/tomfran/urban-sound-classification

UrbanSound8K dataset classification using MLP and CNN.
https://github.com/tomfran/urban-sound-classification

audio-classification cnn keras machine-learning neural-network python tensorflow urbansound8k

Last synced: 4 days ago
JSON representation

UrbanSound8K dataset classification using MLP and CNN.

• Host: GitHub
• URL: https://github.com/tomfran/urban-sound-classification
• Owner: tomfran
• License: mit
• Created: 2021-06-26T10:25:14.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-17T10:16:21.000Z (almost 3 years ago)
• Last Synced: 2023-03-02T14:43:29.271Z (over 1 year ago)
• Topics: audio-classification, cnn, keras, machine-learning, neural-network, python, tensorflow, urbansound8k
• Language: Jupyter Notebook
• Homepage:
• Size: 8.11 MB
• Stars: 6
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Urban sound classification

The goal of the project is to build a neural network capable of classification on the [Urban Sound 8k dataset](https://urbansounddataset.weebly.com/urbansound8k.html).


An in depth overview of the project is present on the [project report](https://github.com/tomfran/urban-sound-classification/blob/main/report/report.pdf).

## Project structure

The project folder is structured as follows:

- **data/** contains processed and raw data. To reproduce results using the dataset, 

put the folds folders inside **data/raw/audio**, then put the metadata file inside **data/raw/metadata**

- **models/** contains trained models, namely the scaler and pca used in the project

- **notebooks/** contains the Jupyter notebooks used execute the code

- **src/** contains **data**, **model** and **utils** sub-folders, with code regarding the different parts 

of the project

- **report/** contains the project report written in Latex

## Requirements

Libraries used in the project are the following: *pandas*, *numpy*, *matplotlib*, *tensorflow*, *librosa*, *dask*, *keras_nightly*, *keras*, *scikit_learn*

 

You can install them using the following command

```[shell]

pip install -r src/requirements.txt

```

## Methodology

The methodology followed in the project can be seen in the various jupyter notebooks.

### Feature extraction and dataset creation

In the [first notebook](https://github.com/tomfran/urban-sound-classification/blob/main/notebooks/01_dataset.ipynb) audio features are extracted using Librosa library and scaling 

is applied. 


In the [second notebook](https://github.com/tomfran/urban-sound-classification/blob/main/notebooks/02_dataset_extended_cnn.ipynb), more features are extracted and PCA feature selection is exploited to reduce the dataset dimensionality.

Also, audio as an image is extracted from the dataset for later training a CNN.

### Cross validation on the training sets

To understand what training set is best suited for the project, 

cross validation is performed on the initial, scaled, extended and pca dataset obtained at the previous step.

The results are presented in the [third notebook](https://github.com/tomfran/urban-sound-classification/blob/main/notebooks/03_cross_validation_mlp.ipynb).

### Hyperparameter tuning 

After selecting the best dataset from the cross validation results, 

a Random Search is performed to optimize the network hyperparameters, 

details about results as well as test set evaluation can be found on the [fourth notebook](https://github.com/tomfran/urban-sound-classification/blob/main/notebooks/04_hyperparameter_tuning_mlp.ipynb).

### CNN training and tuning

The [last notebook](https://github.com/tomfran/urban-sound-classification/blob/main/notebooks/05_cnn.ipynb) presents the results using a convolutional neural 

network on the image dataset obtained in the second notebook.

This notebook was executed on Google Colab for performance reasons.