Where Sound Meets Science. A tool for seeing patterns in sound that would otherwise remain invisible — across language, animal communication, and human expression.
What stories are hidden in the sounds around us? From the distinctive tones of Mandarin to the alarm calls of birds, sound carries meaning that transcends the visible. Symphonic-Joules exists for researchers, linguists, and anyone curious about what sound can reveal, providing computational tools to let you listen deeper.
We analyze sound through the acoustic energy density equation, which governs how sound carries energy through space:
Where:
-
$w$ = acoustic energy density ($\text{J}/\text{m}^3$ ) -
$p$ = sound pressure ($\text{Pa}$ ) -
$\rho$ = medium density ($\text{kg}/\text{m}^3$ ) -
$c$ = speed of sound ($\text{m}/\text{s}$ ) -
$v$ = particle velocity ($\text{m}/\text{s}$ )
This fundamental relationship reveals that sound is energy in motion. It's how we quantify the invisible.
Prerequisites: Python 3.8+ (3.11 recommended for macOS)
# 1. Clone the repository
git clone [https://github.com/JaclynCodes/Symphonic-Joules-a67272a4.git](https://github.com/JaclynCodes/Symphonic-Joules-a67272a4.git)
cd Symphonic-Joules-a67272a4
# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# 3. Install the package
pip install -e .
Symphonic-Joules follows an interface-first philosophy, designed for clear, explicit interactions:
from symphonic_joules import AudioSignal, EnergyCalculator
# Load an audio signal
signal = AudioSignal.from_file("birdsong.wav")
# Calculate acoustic energy density
calculator = EnergyCalculator(
medium_density=1.225, # kg/m³ (air at 20°C)
sound_speed=343.0 # m/s (air at 20°C)
)
energy_density = calculator.compute_energy_density(signal)
print(f"Energy Density: {energy_density:.6f} J/m³")- Birdsong & Animal Communication: Analyze spectral signatures and detect alarm calls to study wildlife behavior.
- Tonal Language Analysis: Use energy-based frequency analysis to map pitch contours in languages like Mandarin, Yoruba, and Thai.
- Emotional & Social Subtext: Quantify the stress, emotion, and confidence encoded in vocal prosody.
- Acoustic Ecology: Process field recordings to monitor forest density, species presence, and environmental shifts.
- Music & Cultural Analysis: Map energy distribution across musical genres and orchestral arrangements.
- Clinical & Accessibility: Analyze vocal patterns for speech therapy, or explore how neurodivergent individuals produce and perceive sound.
To keep this repository clean, all deep-dive information is organized in our documentation hub:
- 📖 Full Documentation — API references, installation guides, and examples.
- 🏗️ Architecture & Data Flow — How the pipeline transforms raw audio into quantifiable patterns.
- 🧪 Testing Philosophy — Read about our Documentation-as-Code approach.
- 🗺️ Project Roadmap — See our multi-phase development plan.
- 🤝 Contributing Guidelines — How to join the project.
Current Phase: 🏗️ Foundation (v0.1.0) | Licensed under MIT
To complete this migration, simply copy the large sections we evicted (like the Testing Philosophy, the Architecture pipeline, and the full multi-phase Roadmap) and paste them into new Markdown files inside your `docs/` folder. This gives your project a clean, professional architecture where everything has its designated space.