Digital Audio Signal Filtering & Noise Reduction

Note: This project is currently a Work in Progress and was initiated as part of the Signal and Systems course. We are actively looking for contributors to help expand its capabilities!

This project is a Python-based Digital Signal Processing (DSP) tool designed to filter and reduce noise in audio signals, specifically targeting .wav files. It applies advanced mathematical filters to remove unwanted frequencies and exports a clean, high-quality audio file.

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Course Context

Developed within the scope of Signal and Systems, this project explores the practical application of:

• Frequency Analysis: Understanding noise characteristics in the frequency domain.
• Filter Design: Implementing Butterworth, Chebyshev, or other digital filters.
• Signal Transformation: Utilizing Fourier Transforms for audio processing.

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Features

• Noise Reduction: Advanced filtering algorithms to clean audio data.
• Waveform Visualization: Before and after plots for visual analysis.
• Support for .wav files: Industry-standard audio format support.
• Modern Tech Stack: Built with leading Python DSP libraries.

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🛠️ Tech Stack & Dependencies

This project utilizes the following industry-standard libraries:

• NumPy: For core mathematical computations and handling audio data.
• SciPy (scipy.signal): For signal processing and generating core filtering algorithms.
• Matplotlib: For visualizing audio data and waveforms.
• Librosa: For advanced audio analysis and feature extraction.
• SoundFile: For reading and writing .wav files.

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Contributing

Since this project is in its early stages, we highly encourage contributions! Whether you're a fellow student or a DSP enthusiast, feel free to:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

Items on our roadmap:

• Support for more audio formats (MP3, FLAC).
• Real-time noise filtering.
• GUI for easier interaction.
• More robust filter types (e.g., adaptive filters).

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Getting Started

1. Clone the Repository

Open your terminal or command prompt and run:

git clone git@github.com:Aaron-pweb/Digital_Audio_Signal_Filtering_and_Noise_Reduction.git
cd Digital_Audio_Signal_Filtering_and_Noise_Reduction

2. Set Up a Virtual Environment (Recommended)

🐧 Linux / 🍎 macOS

python3 -m venv .venv
source .venv/bin/activate

🪟 Windows

python -m venv .venv
.venv\Scripts\activate

3. Install Dependencies

Once the virtual environment is activated, install the required packages:

pip install --upgrade pip
pip install -r requirements.txt

4. Add to Jupyter (ipykernel)

To run the .ipynb notebook files in VS Code or Jupyter, you need to add your virtual environment as a kernel:

pip install ipykernel
python -m ipykernel install --user --name=dsp_env --display-name "Python (DSP Project)"

After running this, open sound-filtter.ipynb and select "Python (DSP Project)" from the kernel/interpreter menu.

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☁️ Running on Google Colab

If you prefer not to install anything locally, you can run this project directly on Google Colab:

1. Upload sound-filtter.ipynb, requirements.txt, and the audio/ folder to your Google Drive.
2. Open the notebook in Colab.
3. Install dependencies in a Colab cell:

   !pip install -r requirements.txt

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🎧 How to Use

1. Place your raw audio files in the audio/ directory.
2. Open sound-filtter.ipynb in your preferred editor (VS Code, Jupyter, or Colab).
3. Follow the steps in the notebook to load, process, and save your clean audio.

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📄 License

This project is open-source and available under the MIT License.