Beginner's Guide to AI Concert Visuals

Automated Approach

Emphasis: AI-driven automation

In this workflow, the system operates with minimal human intervention during the performance. Focus is on robust audio analysis, reliable AI processing, and configuring fallbacks for unexpected situations.

Best for: Consistent performances, smaller production teams, bands with predictable setlists

Hybrid Approach

Emphasis: Balance between AI and human control

Combines automated elements with manual triggering and adjustment. AI handles moment-to-moment reactivity while the operator makes higher-level decisions about scene changes and major transitions.

Best for: Most concert scenarios, balancing reliability with flexibility

Performance-Focused

Emphasis: Visual artist as performer

Treats visual generation as a performance art alongside the music. AI tools augment the visual artist's capabilities rather than replacing them, enabling more expressive and improvised visual performances.

Best for: Experimental music, collaborations where visuals are highlighted, experienced visual artists

Basic Implementation Example

// Simple audio reactivity in JavaScript/p5.js
function draw() {
  // Analyze audio
  let spectrum = fft.analyze();
  let bass = fft.getEnergy("bass");
  let mid = fft.getEnergy("mid");
  let high = fft.getEnergy("treble");
  
  // Map audio to visual parameters
  let bassSize = map(bass, 0, 255, 100, 300);
  let midColor = map(mid, 0, 255, 0, 255);
  let highSpeed = map(high, 0, 255, 0, 5);
  
  // Apply to visuals
  background(0, 10);
  fill(0, midColor, 255, 150);
  ellipse(width/2, height/2, bassSize, bassSize);
  
  // Additional particles based on high frequencies
  for(let i = 0; i < 5; i++) {
    let x = random(width);
    let y = random(height);
    let size = random(2, 10);
    fill(255, 255, 255, 100);
    ellipse(x, y, size * highSpeed, size * highSpeed);
  }
}

Learning Progression

1. Start with direct mappings
   Bass � Size, Treble � Speed, etc.
2. Add smoothing and scaling
   Prevent jerky movements by averaging values over time
3. Create compound mappings
   Multiple audio inputs affecting a single visual parameter
4. Implement trigger-based reactions
   Visual events triggered by detected beats or onsets
5. Integrate machine learning
   Train models to recognize patterns and respond contextually