Evolving Ambient System

Overview

This project explores how ambient visual environments can become self-adjusting and context-aware by merging weather data APIs with live audio analysis. The system generates visuals that respond simultaneously to external atmospheric conditions and internal soundscapes.

The result is not a reactive visualiser that follows sound — it’s a system with two independent inputs that create a combined emergent aesthetic logic.

System Flow

Weather API + Audio Stream
        ↓
   Data Filters
        ↓
TD Particle Engine
        ↓
  Feedback + FX
        ↓
Final Visual Output

Audio Mapping

Audio amplitude and rhythm controlled particle behaviour. Loudness affected intensity and particle speed. The analysis ran in real time with no buffering — the visual responded within a single frame of the audio signal.

Weather Mapping

Weather data was routed through Python APIs and scaled using temperature, humidity, and cloud cover. In Resolume, these values modified contrast, radial blur, and colour wash effects — slower, more pervasive shifts compared to the rapid audio response.

Key Outcomes

• Self-adjusting generative visual engine with dual data input
• Hourly API integration for environmental parameter shifts
• Live audio mapping optimised for low-latency response
• Combined dual data domains into a unified aesthetic logic

---