Update docs — spatial scene, distance estimation, roadmap progress
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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BINAURAL_ROADMAP.md
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BINAURAL_ROADMAP.md
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# Binaural Hearing Roadmap
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## What two mic arrays make possible
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Ranked by impact × feasibility. All build on the existing dual XVF3800 + `/doa` endpoint.
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---
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### Tier 1 — High impact, ready to build now
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**1. Triangulated sound localization + eye gaze**
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- Combine DoA angles from both arrays → compute (x, y) position of sound source
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- Post gaze coordinates to eye service → eyes track the speaker spatially
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- Front/back disambiguation (single array can't tell 30° front from 30° rear)
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- *Prereqs:* Known array positions (measured once), basic trig
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- *Complexity:* Low — ~100 lines of math + a gaze-push thread
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- *Impact:* Huge — eyes actually follow the person, not just shift left/right
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**2. Active speaker tracking with smooth gaze**
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- Continuously track the dominant sound source as it moves
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- Smooth the gaze updates (low-pass filter) so eyes don't jitter
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- When VAD drops, eyes drift back to center (natural idle behavior)
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- *Prereqs:* #1
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- *Complexity:* Low — Kalman filter or exponential smoothing on top of #1
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- *Impact:* Makes her feel present and attentive
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**3. Left/right speaker awareness**
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- Know which side each speaker is on, combine with speaker ID
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- "Alex is on my left" vs "unknown person on my right"
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- Feed into LYRA context so responses can reference spatial relationships
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- *Prereqs:* #1 + existing speaker ID
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- *Complexity:* Medium — associate speaker embeddings with spatial positions
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- *Impact:* Multi-person conversations become spatially grounded
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---
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### Tier 2 — High impact, moderate effort
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**4. Distance estimation (near/far)**
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- Interaural Level Difference (ILD): close sources have bigger volume gap between ears
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- Interaural Time Difference (ITD): measurable with raw mic data (would need 6-channel firmware)
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- Rough bins: intimate (<0.5m), conversational (0.5-2m), across room (2m+)
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- *Prereqs:* #1, calibration with known distances
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- *Complexity:* Medium — ILD from processed channels is easy, ITD needs raw mics
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- *Impact:* Interaction style adapts to proximity (whisper vs. room voice)
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**5. Multi-speaker separation + selective attention**
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- Lock each array's beam to a different speaker simultaneously
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- Active speaker gets primary audio feed (wake word, transcription)
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- Secondary speaker monitored for interruptions or wake word
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- Switch attention on cue ("Hey Vivi" from the other side)
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- *Prereqs:* #3, understanding of XVF3800 beam steering commands
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- *Complexity:* Medium-high — need to control beamformer direction per-array
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- *Impact:* Natural multi-person conversations, not just one-at-a-time
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**6. Spatial audio scene mapping**
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- Build a persistent map: "TV at 270°, door at 90°, kitchen at 180°"
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- Learn from repeated sound sources over hours/days
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- Detect anomalies: "sound from an unusual direction"
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- *Prereqs:* #1, persistent storage, classification by direction
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- *Complexity:* Medium — accumulate (direction, category) pairs, cluster over time
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- *Impact:* Environmental awareness, contextual anomaly detection
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---
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### Tier 3 — Cool, needs more infrastructure
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**7. Cocktail party spatial filtering**
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- When multiple sound sources active, use both arrays to null out interference
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- Focus beam on target speaker, suppress others spatially
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- *Prereqs:* #5, possibly raw mic access (6-channel firmware)
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- *Complexity:* High — adaptive beamforming, may need custom DSP
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- *Impact:* Works in noisy environments (music playing, multiple people)
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**8. Sound event localization (what + where)**
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- Combine YAMNet classification with triangulated position
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- "Dog bark from the backyard direction" not just "dog bark"
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- Spatial history: timeline of what happened where
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- *Prereqs:* #1, #6
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- *Complexity:* Medium — merge classification results with position data
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- *Impact:* Rich environmental narrative for LYRA context
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**9. Head orientation inference**
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- If a known sound source is at a fixed position, infer which way the head is "facing"
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- Useful if the skull ever gets a rotating mount
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- *Prereqs:* #6 (known spatial map)
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- *Complexity:* Low math, but needs stable reference points
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- *Impact:* Low for now (head doesn't turn), future-proofing
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**10. Binaural recording for training data**
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- Record stereo audio preserving spatial information (left ear / right ear)
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- Training corpus for spatial audio models, being0 sensor data
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- *Prereqs:* Just dual streams saved to stereo WAV
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- *Complexity:* Low — already have both streams
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- *Impact:* Long-term value for L-Vixy-5 training
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---
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### Tier 4 — Research / future
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**11. Learned spatial attention**
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- Train a model to decide where to attend based on context
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- Input: both DoA angles, VAD states, current emotional state, conversation history
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- Output: beam steering + gaze direction
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- *Prereqs:* #5, #6, training data from #10
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- *Complexity:* High — ML training pipeline
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- *Impact:* Autonomous attention that feels natural, not rule-based
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**12. Interaural time difference (ITD) processing**
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- Raw mic access (6-channel firmware) enables sub-sample timing analysis
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- More precise localization than DoA alone, especially at low frequencies
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- *Prereqs:* 6-channel firmware (need to verify LED control works with it first)
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- *Complexity:* High — signal processing, cross-correlation
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- *Impact:* Lab-grade localization accuracy
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---
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## Implementation order
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```
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✅ #1 Triangulation + gaze — done (spatial.py, auto-select beam DoA)
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✅ #2 Smooth tracking — done (exponential smoothing + idle drift)
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✅ #3 Speaker-side awareness — done (Resemblyzer loaded, ready for enrollment)
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✅ #4 Distance estimation — done (ILD + triangulation fusion, proximity zones)
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✅ #6 Spatial scene mapping — done (spatial_scene.py, persistent, anomaly detection)
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✅ #8 Sound event localization — done (what + where + when via /scene/events)
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✅ #10 Binaural recording — done (opt-in via BINAURAL_RECORD=1)
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#5 Multi-speaker separation
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#7 Cocktail party filtering
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#7 Cocktail party filtering
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#11 Learned attention
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```
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## Notes
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- Items #1-3 can be built in a single session
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- The eye service already accepts gaze via `POST /gaze {"x": N, "y": N}`
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- DoA is already polled at 10Hz via `/doa` endpoint
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- Array separation distance needs to be measured once and stored in config
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- All of this feeds into the being0 "shaped by experience" philosophy
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55
README.md
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README.md
@@ -18,26 +18,28 @@ Binaural hearing service for Vixy's physical head. Dual mic arrays with spatial
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└────────────┬───────────────────────────────┘
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└────────────┬───────────────────────────────┘
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▼
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▼
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DualAudioStream (audio_stream.py)
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DualAudioStream (audio_stream.py)
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best-beam selection (energy-based)
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best-beam selection (energy-based, 10% hysteresis)
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│
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┌──────────────────┼──────────────────────┐
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▼ ▼ ▼
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Porcupine YAMNet Binaural
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wake word (Edge TPU) Recorder
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"Hey Vivi" 521 classes stereo WAV
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▼ ▼
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Record + Speaker ID
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Transcribe (Resemblyzer)
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via EarTail │
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▼
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Spatial Tracker (spatial.py)
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DoA → triangulation → ILD distance
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→ smooth gaze → proximity zones
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│
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│
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┌────────────┼────────────────┐
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┌────────────┼────────────────┐
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▼ ▼ ▼
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▼ ▼ ▼
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Porcupine YAMNet Binaural
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Eye Service Spatial Scene USB Control
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wake word (Edge TPU) Recorder
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POST /gaze (spatial_scene) (xvf3800.py)
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"Hey Vivi" 521 classes stereo WAV
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eyes follow what+where map LEDs + DoA
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▼ ▼
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the speaker anomaly detect per-array
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Record + Speaker ID
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Transcribe (Resemblyzer)
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via EarTail
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│
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┌────────────┼────────────────┐
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▼ ▼ ▼
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Spatial Tracker (spatial.py) USB Control (xvf3800.py)
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DoA → triangulation LEDs + DoA polling
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→ smooth gaze per-array control
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▼
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Eye Service (port 8780)
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POST /gaze → eyes follow speaker
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```
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```
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## Features
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## Features
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| Wake word detection | Porcupine | CPU | Needs Picovoice key |
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| Wake word detection | Porcupine | CPU | Needs Picovoice key |
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| Sound classification | sound_id.py | Coral Edge TPU | 521 classes, ~2ms |
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| Sound classification | sound_id.py | Coral Edge TPU | 521 classes, ~2ms |
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| Speaker identification | speaker_id.py | CPU (Resemblyzer) | Enrollment via API |
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| Speaker identification | speaker_id.py | CPU (Resemblyzer) | Enrollment via API |
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| Spatial tracking | spatial.py | USB control | Triangulated gaze |
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| Spatial tracking | spatial.py | USB control | Triangulated gaze + ILD distance |
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| Best-beam selection | audio_stream.py | 2× XVF3800 | Energy-based |
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| Distance estimation | spatial.py | audio energy | Proximity zones (intimate/conversational/across_room/far) |
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| Spatial scene mapping | spatial_scene.py | — | Learns where sounds come from, anomaly detection |
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| Sound event localization | spatial_scene.py | — | What + where + when log |
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| Best-beam selection | audio_stream.py | 2× XVF3800 | Energy-based, 10% hysteresis |
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| LED control | xvf3800.py | WS2812 rings | DoA/solid/breath |
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| LED control | xvf3800.py | WS2812 rings | DoA/solid/breath |
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| Binaural recording | binaural_recorder.py | 2× XVF3800 | Stereo WAV segments |
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| Binaural recording | binaural_recorder.py | 2× XVF3800 | Stereo WAV segments (opt-in) |
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## Installation
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## Installation
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| Endpoint | Method | Description |
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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|----------|--------|-------------|
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| `/doa` | GET | DoA from both arrays + triangulated position + gaze |
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| `/doa` | GET | DoA from both arrays + triangulated position + gaze + distance + proximity |
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| `/devices` | GET | XVF3800 connection status, serials, ALSA devices |
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| `/devices` | GET | XVF3800 connection status, serials, ALSA devices |
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| `/scene` | GET | Learned spatial scene (usual direction per category) + last anomaly |
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| `/scene/events` | GET | Recent sound events with what + where + when (query: seconds, category) |
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| `/scene/heatmap` | GET | Per-category angular distribution for visualization |
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### Sound
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### Sound
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headmic/
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headmic/
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├── headmic.py # Main FastAPI service
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├── headmic.py # Main FastAPI service
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├── audio_stream.py # Dual arecord streams + best-beam selection
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├── audio_stream.py # Dual arecord streams + best-beam selection
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├── spatial.py # Triangulation + smooth gaze tracking
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├── spatial.py # Triangulation + ILD distance + smooth gaze + proximity
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├── spatial_scene.py # Spatial audio scene map + anomaly detection
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├── xvf3800.py # USB vendor control (DoA + LEDs)
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├── xvf3800.py # USB vendor control (DoA + LEDs)
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├── sound_id.py # YAMNet sound classification (CPU/Edge TPU)
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├── sound_id.py # YAMNet sound classification (CPU/Edge TPU)
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├── speaker_id.py # Resemblyzer speaker identification
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├── speaker_id.py # Resemblyzer speaker identification
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@@ -260,6 +269,8 @@ Commands use USB vendor control transfers: `wValue = cmdid`, `wIndex = resid`.
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- Read responses have a 1-byte status header before data
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- Read responses have a 1-byte status header before data
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- Read wLength must be `count * type_size + 1` (exact, not rounded up)
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- Read wLength must be `count * type_size + 1` (exact, not rounded up)
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- `DOA_VALUE` (resid=20, cmdid=18) is sluggish/cached — use `AUDIO_MGR_SELECTED_AZIMUTHS` (resid=35, cmdid=11) for real-time tracking
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- `DOA_VALUE` (resid=20, cmdid=18) is sluggish/cached — use `AUDIO_MGR_SELECTED_AZIMUTHS` (resid=35, cmdid=11) for real-time tracking
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- `AUDIO_MGR_SELECTED_AZIMUTHS` returns 2 floats (radians): index 0 = processed DoA (NaN = no speech = VAD indicator), index 1 = auto-select beam (always tracks strongest source)
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- `AEC_SPENERGY_VALUES` (resid=33, cmdid=80) is always zero on 2-channel firmware — don't rely on it
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- **2-channel firmware only** — 6-channel firmware silently ignores LED/control commands
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- **2-channel firmware only** — 6-channel firmware silently ignores LED/control commands
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---
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---
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