Bird Voice Classifier

Overview

The Bird Voice Classifier is a machine learning project that aims to classify bird species based on their vocalizations. The project uses audio recordings of bird calls and songs to train a model that can identify different bird species.

Table of Contents

• Project Description
• Installation
• Usage
• Dataset
• Model
• Results
• Contributing
• License
• Acknowledgements

Project Description

The Bird Voice Classifier leverages audio processing and machine learning techniques to recognize bird species from audio recordings. The project includes the following components:

• Data preprocessing: Converting raw audio files into features suitable for machine learning.
• Model training: Training a neural network model using the extracted features.
• Model evaluation: Assessing the model's performance on a test dataset.
• Inference: Using the trained model to classify new audio recordings.

Installation

To set up the Bird Voice Classifier project on your local machine, follow these steps:

1. Clone the repository:

git clone https://github.com/your-username/bird-voice-classifier.git
cd bird-voice-classifier

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  
# On Windows, use 
venv\Scripts\activate

3. Install the required packages:

pip install -r requirements.txt

4. Download the dataset:

Download the bird audio dataset and place it in the data directory. Ensure the dataset is organized appropriately for training and testing.

Usage

To train the model, use the following command:

python train.py

To evaluate the model, use the following command:

python evaluate.py

To classify a new audio file, use the following command:

python classify.py --file path/to/audio/file.wav

Dataset

The dataset used for this project consists of audio recordings of bird calls and songs. Each recording is labeled with the corresponding bird species. The dataset is divided into training, validation, and test sets.

Model

The Bird Voice Classifier uses a convolutional neural network (CNN) to process the audio features and classify the bird species. The model architecture includes several convolutional layers followed by fully connected layers.

Results

The model achieves an accuracy of X% on the test dataset. Below is a summary of the model's performance:

• Training Accuracy: X%
• Validation Accuracy: X%
• Test Accuracy: X%

Contributing

Contributions are welcome! If you have any improvements or suggestions, please create an issue or submit a pull request.

• Fork the repository.
• Create a new branch: git checkout -b feature-branch-name.
• Make your changes and commit them: git commit -m 'Add some feature'.
• Push to the branch: git push origin feature-branch-name.
• Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• Original Dataset - Link to the source of the bird audio dataset.
• Librosa - A Python library for audio and music analysis.
• TensorFlow - An open-source machine learning framework.

Feel free to modify the contents of this README.md file according to your project's specific details and requirements.