Audio-classification

Audio Classification Project Using Deep Learning

Audio Classification Project Using Deep Learning is a sophisticated machine learning project aimed at classifying audio signals into predefined categories. This project utilizes deep learning techniques to analyze and classify audio data, providing accurate and efficient solutions for various applications such as speech recognition, music genre classification, and environmental sound identification.

Table of Contents

1. Overview
2. Features
3. Dataset
4. Methodology
5. Installation
6. Usage
7. Technologies Used
8. Workflows
9. Contributing
10. License
11. Contact

Overview

The Audio Classification Project Using Deep Learning aims to classify different types of audio signals using advanced deep learning models. By leveraging convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the project achieves high accuracy in identifying and categorizing various audio inputs.

Features

• Audio Signal Processing: Efficiently processes raw audio signals for classification.
• Multi-Category Classification: Supports classification across multiple predefined categories.
• Real-Time Classification: Capable of classifying audio signals in real-time.
• High Accuracy: Utilizes state-of-the-art deep learning models to achieve high classification accuracy.
• Customizable Model: Allows customization of the model architecture and parameters based on specific requirements.

Dataset

The project uses a diverse dataset composed of various audio samples across different categories. Examples of datasets that can be used include:

• UrbanSound8K: A dataset containing 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes.
• GTZAN: A dataset for music genre classification containing 1000 audio tracks categorized into 10 genres.
• ESC-50: A dataset of 2000 environmental audio recordings organized into 50 classes.

Methodology

The project follows a structured approach to classify audio signals accurately:

1. Data Ingestion: Gather audio samples from various sources and categorize them.
2. Data Transformation: Clean and preprocess the audio data, including noise reduction, normalization, and feature extraction (e.g., Mel spectrograms).
3. Data Validation: Ensure the quality and consistency of the data through validation techniques.
4. Model Training: Train the models using the preprocessed dataset, optimizing for accuracy and performance.
5. Model Evaluation: Evaluate the models using metrics like accuracy, precision, recall, and F1 score.

Installation

Follow these steps to set up the project on your local machine:

1. Clone the Repository:

    git clone https://github.com/yash-raj202134/AudioClassificationProject.git
   

2. Install Dependencies:
   • Create and activate a virtual environment:

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

   • Install the required packages:

       pip install -r requirements.txt
     

3. Prepare Data:
   • Ensure the dataset is available in the specified directory.
   • Follow the instructions in the project documentation to format and preprocess the dataset.

Usage

To use the Audio Classification Project, follow these steps:

1. Run the Preprocessing Script:

    python preprocess_data.py
   

2. Train the Model:

    python train_model.py
   

3. Evaluate the Model:

    python evaluate_model.py
   

4. Classify New Audio Samples:

    python classify_audio.py --audio_path path/to/audio/file
   

Technologies Used

• Python: The primary programming language for the project.
• TensorFlow/Keras: Used for building and training the deep learning models.
• Librosa: Utilized for audio processing and feature extraction.
• Pandas/Numpy: Employed for data manipulation and analysis.
• Scikit-learn: Used for model evaluation and performance metrics.
• Jupyter Notebooks: For exploratory data analysis and experimentation.

Workflows

The project follows a detailed workflow for updating and managing configurations and components:

1. Update config.yaml: Modify the configuration settings as needed for the project.
2. Update params.yaml: Adjust the parameters for model training and evaluation.
3. Update the entity: Ensure that the data entities are correctly defined and updated.
4. Update the configuration manager in src config: Manage and update the configuration settings within the source code.
5. Update the components: Make necessary changes to the components involved in data processing and model training.
6. Update the pipeline: Modify the data pipeline to reflect changes in data processing and model training steps.
7. Update the main.py: Ensure that the main script incorporates all updates and runs the project seamlessly.

Contributing

We welcome contributions from the community! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch with your changes.
3. Make a pull request to the main branch.

For major changes, please open an issue first to discuss what you would like to change.

License

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

Contact

Feel free to reach out with any questions or feedback:

• Author: Yash Raj
• Email: yashraj3376@gmail.com
• LinkedIn: Yash Raj

Acknowledgments

• Thanks to the contributors and the open-source community for their invaluable support.
• Special thanks to my mentors and peers who provided guidance and feedback throughout the project.

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Happy Coding!

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