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https://github.com/mike014/audio-classification-
This is a prototype Django application that allows users to upload audio files and classify them using machine learning techniques.
https://github.com/mike014/audio-classification-
ai audio django django-application machine-learning mfcc pca python
Last synced: 3 months ago
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This is a prototype Django application that allows users to upload audio files and classify them using machine learning techniques.
- Host: GitHub
- URL: https://github.com/mike014/audio-classification-
- Owner: Mike014
- License: gpl-3.0
- Created: 2024-09-04T15:52:24.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-04T15:59:01.000Z (4 months ago)
- Last Synced: 2024-10-11T07:05:15.985Z (3 months ago)
- Topics: ai, audio, django, django-application, machine-learning, mfcc, pca, python
- Language: Python
- Homepage:
- Size: 7.43 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Audio Classification App
## Overview
This is a prototype Django application that allows users to upload audio files and classify them using machine learning techniques. The app uses Principal Component Analysis (PCA) for feature reduction and K-Nearest Neighbors (KNN) for classification. The application is designed to be extendable, allowing for the addition of more data and classes in the future.
## Features
- **Upload Audio Files**: Users can upload audio files through a web interface.
- **Feature Extraction**: Extracts Mel-Frequency Cepstral Coefficients (MFCC) from the audio files.
- **Dimensionality Reduction**: Uses PCA to reduce the dimensionality of the extracted features.
- **Classification**: Classifies the audio files using a pre-trained KNN model.
- **Results Display**: Shows the predicted class of the uploaded audio file.
## Installation
1. **Clone the Repository**:
```bash
git clone https://github.com/yourusername/audio_classification.git
cd audio_classification
```
2. **Create a Virtual Environment**:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
4. **Run Migrations**:
```bash
python manage.py migrate
```
5. **Start the Development Server**:
```bash
python manage.py runserver
```
6. **Access the Application**:
Open your web browser and go to `http://127.0.0.1:8000/`.
## Usage
1. **Upload an Audio File**:
- Navigate to the upload page.
- Select an audio file and click the "Upload" button.
2. **View Classification Results**:
- After uploading, you will be redirected to a results page showing the predicted class of the audio file.
## Concepts
### Feature Extraction
The application uses the `librosa` library to extract MFCC features from the audio files. MFCCs are commonly used in audio processing to represent the short-term power spectrum of a sound.
### Dimensionality Reduction
PCA is used to reduce the dimensionality of the extracted features. This helps in reducing noise and simplifying the data for classification.
### Classification
A pre-trained KNN model is used to classify the audio files. KNN is a simple, yet effective, classification algorithm that assigns a class based on the majority class of the nearest neighbors.
## Extending the Application
This application is a prototype and can be extended in several ways:
- **Add More Data**: Collect and label more audio files to improve the accuracy of the model.
- **Extract Additional Features**: Use other audio features like chroma, spectral contrast, etc.
- **Use Advanced Models**: Experiment with more complex models like Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN).
## Screenshots
### Upload Page
### Results Page
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Acknowledgements
- [librosa](https://librosa.org/) for audio processing.
- [scikit-learn](https://scikit-learn.org/) for machine learning algorithms.
- [Django](https://www.djangoproject.com/) for the web framework.