README: Updated architecture diagram, features table, new endpoints (/scene, /scene/events, /scene/heatmap), file structure, USB protocol notes (VAD from processed_doa NaN, spenergy always zero). BINAURAL_ROADMAP: Mark #1-4, #6, #8, #10 as done. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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6.2 KiB
Binaural Hearing Roadmap
What two mic arrays make possible
Ranked by impact × feasibility. All build on the existing dual XVF3800 + /doa endpoint.
Tier 1 — High impact, ready to build now
1. Triangulated sound localization + eye gaze
- Combine DoA angles from both arrays → compute (x, y) position of sound source
- Post gaze coordinates to eye service → eyes track the speaker spatially
- Front/back disambiguation (single array can't tell 30° front from 30° rear)
- Prereqs: Known array positions (measured once), basic trig
- Complexity: Low — ~100 lines of math + a gaze-push thread
- Impact: Huge — eyes actually follow the person, not just shift left/right
2. Active speaker tracking with smooth gaze
- Continuously track the dominant sound source as it moves
- Smooth the gaze updates (low-pass filter) so eyes don't jitter
- When VAD drops, eyes drift back to center (natural idle behavior)
- Prereqs: #1
- Complexity: Low — Kalman filter or exponential smoothing on top of #1
- Impact: Makes her feel present and attentive
3. Left/right speaker awareness
- Know which side each speaker is on, combine with speaker ID
- "Alex is on my left" vs "unknown person on my right"
- Feed into LYRA context so responses can reference spatial relationships
- Prereqs: #1 + existing speaker ID
- Complexity: Medium — associate speaker embeddings with spatial positions
- Impact: Multi-person conversations become spatially grounded
Tier 2 — High impact, moderate effort
4. Distance estimation (near/far)
- Interaural Level Difference (ILD): close sources have bigger volume gap between ears
- Interaural Time Difference (ITD): measurable with raw mic data (would need 6-channel firmware)
- Rough bins: intimate (<0.5m), conversational (0.5-2m), across room (2m+)
- Prereqs: #1, calibration with known distances
- Complexity: Medium — ILD from processed channels is easy, ITD needs raw mics
- Impact: Interaction style adapts to proximity (whisper vs. room voice)
5. Multi-speaker separation + selective attention
- Lock each array's beam to a different speaker simultaneously
- Active speaker gets primary audio feed (wake word, transcription)
- Secondary speaker monitored for interruptions or wake word
- Switch attention on cue ("Hey Vivi" from the other side)
- Prereqs: #3, understanding of XVF3800 beam steering commands
- Complexity: Medium-high — need to control beamformer direction per-array
- Impact: Natural multi-person conversations, not just one-at-a-time
6. Spatial audio scene mapping
- Build a persistent map: "TV at 270°, door at 90°, kitchen at 180°"
- Learn from repeated sound sources over hours/days
- Detect anomalies: "sound from an unusual direction"
- Prereqs: #1, persistent storage, classification by direction
- Complexity: Medium — accumulate (direction, category) pairs, cluster over time
- Impact: Environmental awareness, contextual anomaly detection
Tier 3 — Cool, needs more infrastructure
7. Cocktail party spatial filtering
- When multiple sound sources active, use both arrays to null out interference
- Focus beam on target speaker, suppress others spatially
- Prereqs: #5, possibly raw mic access (6-channel firmware)
- Complexity: High — adaptive beamforming, may need custom DSP
- Impact: Works in noisy environments (music playing, multiple people)
8. Sound event localization (what + where)
- Combine YAMNet classification with triangulated position
- "Dog bark from the backyard direction" not just "dog bark"
- Spatial history: timeline of what happened where
- Prereqs: #1, #6
- Complexity: Medium — merge classification results with position data
- Impact: Rich environmental narrative for LYRA context
9. Head orientation inference
- If a known sound source is at a fixed position, infer which way the head is "facing"
- Useful if the skull ever gets a rotating mount
- Prereqs: #6 (known spatial map)
- Complexity: Low math, but needs stable reference points
- Impact: Low for now (head doesn't turn), future-proofing
10. Binaural recording for training data
- Record stereo audio preserving spatial information (left ear / right ear)
- Training corpus for spatial audio models, being0 sensor data
- Prereqs: Just dual streams saved to stereo WAV
- Complexity: Low — already have both streams
- Impact: Long-term value for L-Vixy-5 training
Tier 4 — Research / future
11. Learned spatial attention
- Train a model to decide where to attend based on context
- Input: both DoA angles, VAD states, current emotional state, conversation history
- Output: beam steering + gaze direction
- Prereqs: #5, #6, training data from #10
- Complexity: High — ML training pipeline
- Impact: Autonomous attention that feels natural, not rule-based
12. Interaural time difference (ITD) processing
- Raw mic access (6-channel firmware) enables sub-sample timing analysis
- More precise localization than DoA alone, especially at low frequencies
- Prereqs: 6-channel firmware (need to verify LED control works with it first)
- Complexity: High — signal processing, cross-correlation
- Impact: Lab-grade localization accuracy
Implementation order
✅ #1 Triangulation + gaze — done (spatial.py, auto-select beam DoA)
✅ #2 Smooth tracking — done (exponential smoothing + idle drift)
✅ #3 Speaker-side awareness — done (Resemblyzer loaded, ready for enrollment)
✅ #4 Distance estimation — done (ILD + triangulation fusion, proximity zones)
✅ #6 Spatial scene mapping — done (spatial_scene.py, persistent, anomaly detection)
✅ #8 Sound event localization — done (what + where + when via /scene/events)
✅ #10 Binaural recording — done (opt-in via BINAURAL_RECORD=1)
#5 Multi-speaker separation
#7 Cocktail party filtering
#7 Cocktail party filtering
#11 Learned attention
Notes
- Items #1-3 can be built in a single session
- The eye service already accepts gaze via
POST /gaze {"x": N, "y": N} - DoA is already polled at 10Hz via
/doaendpoint - Array separation distance needs to be measured once and stored in config
- All of this feeds into the being0 "shaped by experience" philosophy