The system is built from a small but expressive set of recurring primitives:
Spatial Audio Object (SAO) / Sound Token
A semantic entity rendered in 3D audio space with identity, position, and state.
Visual Scene Token (VST)
A detected object or feature extracted from camera or sensor input.
Spatial Audio Field / Auditory Field
The full composite 360° sound environment formed by all SAOs.
Spatial Anchor
A fixed physical reference (phone, NFC, QR, stand) defining coordinate origin.
Head Orientation Vector (Attention Vector)
Continuous directional input used for selection, zooming, and filtering.
Directional Binding / Attention Gating
Mechanism that activates or amplifies sound objects within a “gaze cone.”
Layered Audio Stack
- identity layer (what it is)
- spatial layer (where it is)
- contextual layer (what it means / state)
Semantic Compression Unit
Transformation from high-dimensional visual reality → low-dimensional auditory motifs.
Sonic Signature / Micro-melody
Stable auditory identity encoding object class, affordance, or state.
Temporal Micro-Signal
Short pulses replacing narration for rapid auditory scanning.
Pacing Field
Continuous control of cognitive tempo (scrub, pause, accelerate).
HOW THE CONCEPT WORKS
At runtime, SSAS behaves like a perceptual pipeline + interaction field:
1. Perception Layer (Vision / Sensors)
- Camera and/or sensors capture environment
- AI performs object detection, depth estimation, scene graph generation
- Outputs structured Visual Scene Tokens (VSTs)
2. Semantic Translation Layer
- VSTs are mapped into:
- object categories
- affordances (obstacle, path, person, hazard)
- relational structure (distance, motion, intent)
This creates a perceptual graph:
- nodes = objects
- edges = spatial/semantic relations
3. Sonic Encoding Layer
Each node becomes a Spatial Audio Object (SAO):
- position → binaural/HRTF placement
- distance → loudness + spectral blur + pulse rate
- class → timbral family or micro-melody
- motion → rhythmic drift or modulation
The environment becomes a 360° auditory map rather than a visual scene.
4. Attention & Interaction Layer
Interaction is continuous:
- Head turn = scanning / selection
- Dwell time = commitment / focus lock
- Micro-movements = scrubbing through semantic space
- Head shake (in some variants) = rejection/correction signal
This replaces UI “clicking” with perceptual navigation.
5. Layer Control & Compression
To avoid overload:
- background objects collapse into ambient fields
- only high-salience entities become fully articulated SAOs
- multiple resolution levels exist (global / object / detail)
6. AI Feedback Loop
In advanced variants:
- system adapts sound density to cognitive load
- user attention patterns reshape rendering priorities
- optional social/emotional signals modify tone or urgency
The system becomes a closed loop between perception and environment rendering.