speqtr 0.1.0

Real-time audio equalizer and spectrum analyzer

Real-time Audio Equalizer & Spectrum Analyzer

A professional-grade, high-fidelity audio analysis and equalization tool built with Python. This application captures real-time audio input, processes it through a 10-band peaking equalizer, and provides live visualizations of both the waveform and frequency spectrum.

✨ Features

• Professional 10-Band EQ: Standardized bands (31Hz to 16kHz) with ±12dB gain control.
• Accurate Spectrum Analysis: Real-time FFT with proper dBFS normalization.
• Visual Overlays:
  • Live EQ Curve: See exactly how your filters are shaping the audio.
  • Peak Hold: Tracks and decays from maximum peaks for better level monitoring.
• Audio Presets: Quick-access profiles like Bass Boost, Treble Boost, V-Shape, and Vocal.
• Modern Dark UI: Sleek, high-contrast interface designed for professional environments.
• Thread-Safe Core: Robust audio capturing using PyAudio and threading to ensure zero-flicker performance.

🛠️ Tech Stack

• Language: Python 3.10+
• Audio Engine: PyAudio (PortAudio wrapper)
• DSP: NumPy & SciPy (Signal processing)
• GUI: Tkinter (Themed with ttk)
• Graphics: Matplotlib (High-performance plotting)

🚀 Getting Started

Prerequisites

You'll need Python installed. It is recommended to use a virtual environment.

Installation

1. Clone the repository:

   git clone https://github.com/Iyeba-Kallon/realtime-audio-equalizer-spectrum-analyzer.git
   cd realtime-audio-equalizer-spectrum-analyzer
   

2. Install dependencies:

   pip install -r requirements.txt
   

   Note: On some systems, you may need to install portaudio development headers separately (e.g., sudo apt install libportaudio2 on Linux).

Running the App

Simply run the main script:

python main.py

📖 How it Works

1. Capture: The AudioEngine opens a stream via PyAudio to capture system or microphone input.
2. DSP Pipeline:
   • Audio is converted to normalized float32.
   • Each EQ band is a second-order IIR peaking filter (RBJ design).
   • Filters are applied in series to the incoming chunks.
3. Analysis: An FFT (Fast Fourier Transform) is computed with a Hanning window to generate the magnitude spectrum.
4. Visualization: The GUI thread pulls the processed data through a thread-safe lock and updates the Matplotlib plots at ~30 FPS.

🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request for new features (like recording, output playback, or more visualizer types).

📄 License

This project is licensed under the MIT License.