Update docs — complete binaural roadmap (10/12 features)
BINAURAL_ROADMAP: Full status update with implementation details, three-signal localization table, key discoveries section. README: Updated features table (ITD, multi-speaker, cocktail party), new API endpoints (/speakers/tracked, /speakers/focus), file structure. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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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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All features built on dual XVF3800 arrays, 175mm apart on the skull.
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---
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### Tier 1 — High impact, ready to build now
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### Tier 1 — High impact ✅
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**1. Triangulated sound localization + eye gaze**
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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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- Uses `AUDIO_MGR_SELECTED_AZIMUTHS` auto-select beam (not sluggish `DOA_VALUE`)
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- VAD from processed_doa being NaN/non-NaN
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- *Module:* `spatial.py`
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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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**2. Active speaker tracking with smooth gaze** ✅
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- Exponential smoothing (α=0.4) prevents jitter
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- Idle drift back to center after 1.5s of silence
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- Gaze pushed to eye service at ≤2Hz with 5px min delta
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- *Module:* `spatial.py`, `headmic.py` (`doa_track_loop`)
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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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**3. Left/right speaker awareness** ✅
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- Resemblyzer speaker ID integrated, ready for enrollment
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- Spatial position associated with recognized speaker
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- `POST /speakers/enroll-from-mic?name=Alex` to enroll
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- *Module:* `speaker_id.py`, `spatial.py`
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---
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### Tier 2 — High impact, moderate effort
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### Tier 2 — High impact ✅
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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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**4. Distance estimation (near/far)** ✅
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- ILD (Interaural Level Difference): volume gap between ears → distance
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- Fused with triangulated distance (70/30 weight)
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- Proximity zones: intimate (<0.5m), conversational (0.5-2m), across_room (2-5m), far (>5m)
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- *Module:* `spatial.py` (`_compute_ild`, `_ild_to_distance`, `_classify_proximity`)
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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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**5. Multi-speaker separation + selective attention** ✅
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- Tracks up to 2 speakers simultaneously by DoA angle (30°+ separation required)
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- Locks XVF3800 fixed beams onto each speaker after 1s stability
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- Auto-releases to free-running mode after 3s of single speaker
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- Beam gating silences the non-speaking beam
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- `GET /speakers/tracked` — positions, beam state, lock status
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- *Module:* `multi_speaker.py`, `xvf3800.py` (beam steering commands)
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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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**6. Spatial audio scene mapping** ✅
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- Persistent map of where each sound category usually comes from (30° bins)
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- Circular mean for usual direction, anomaly detection at 90°+ deviation
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- Saves to `~/.vixy/scene_map.json` across restarts
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- `GET /scene` — learned directions per category + anomalies
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- `GET /scene/events` — recent what+where+when log
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- `GET /scene/heatmap` — angular distribution for visualization
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- *Module:* `spatial_scene.py`
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---
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### Tier 3 — Cool, needs more infrastructure
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### Tier 3 — Advanced ✅
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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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**7. Cocktail party spatial filtering** ✅
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- When 2 speakers tracked, audio focus locks to target speaker's side
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- Non-target beam suppressed via XVF3800 beam gating
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- Auto-switches target when current goes silent and other starts talking
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- Manual focus via `POST /speakers/focus?speaker=0|1`
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- `DualAudioStream.focus_side` overrides energy-based beam selection
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- *Module:* `multi_speaker.py`, `audio_stream.py` (`focus_side`)
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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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**8. Sound event localization (what + where)** ✅
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- YAMNet classification merged with triangulated position
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- Every classified sound logged with angle, distance, proximity, side
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- "Speech from 75° at conversational distance" not just "speech"
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- *Module:* `spatial_scene.py` (`SoundEvent`, `observe`)
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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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**9. Head orientation inference** ⏭️
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- Needs fixed reference points from scene map + rotating head mount
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- Math is trivial once prerequisites exist
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- *Prereqs:* #6, physical rotating mount
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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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**10. Binaural recording for training data** ✅
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- Records left/right ear streams as stereo WAV in 5-minute segments
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- Opt-in via `BINAURAL_RECORD=1` environment variable
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- `GET /recording` — stats (segments, total seconds)
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- *Module:* `binaural_recorder.py`
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---
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### Tier 4 — Research / future
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### Tier 4 — Research
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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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**11. Learned spatial attention** ⏭️
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- Train a model: (DoA, VAD, emotion, history) → beam steering + gaze
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- Needs training data from #10 running for days/weeks
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- *Prereqs:* #5, #6, #10 data collection
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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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**12. ITD (Interaural Time Difference) processing** ✅
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- Cross-correlates left/right ear processed audio (512 samples, ~32ms window)
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- Finds sub-millisecond delay → bearing angle via speed of sound
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- At 16kHz, 175mm: resolution ~62.5μs/sample ≈ 7° per sample
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- Works with 2-channel firmware (no raw mics needed — correlates processed channels)
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- Third independent angle estimate alongside DoA and ILD
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- *Module:* `spatial.py` (`_compute_itd`)
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---
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## Implementation order
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## Implementation status
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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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✅ #1 Triangulation + gaze
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✅ #2 Smooth tracking
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✅ #3 Speaker-side awareness
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✅ #4 Distance estimation + proximity zones
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✅ #5 Multi-speaker separation + beam steering
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✅ #6 Spatial audio scene mapping + anomaly detection
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✅ #7 Cocktail party spatial filtering
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✅ #8 Sound event localization (what + where + when)
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⏭️ #9 Head orientation inference (needs rotating mount)
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✅ #10 Binaural recording (opt-in)
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⏭️ #11 Learned spatial attention (needs training data)
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✅ #12 ITD cross-correlation
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```
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## Notes
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10 of 12 features implemented in one session. The remaining two (#9, #11) need
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physical hardware changes or training data that accumulates over time.
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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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## Three-signal localization
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The system now fuses three independent spatial estimates:
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| Signal | Source | Strength | Best for |
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|--------|--------|----------|----------|
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| **DoA** | XVF3800 beamformer (auto-select beam) | Best overall | Horizontal angle |
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| **ILD** | Volume difference between ears | Good for near sources | Distance + rough angle |
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| **ITD** | Cross-correlation delay between ears | Good at low frequencies | Precise angle |
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Combined, they give more robust localization than any single signal — the same
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three cues human hearing uses.
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## Key discoveries during development
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1. `DOA_VALUE` (resid=20) is sluggish — use `AUDIO_MGR_SELECTED_AZIMUTHS` (resid=35)
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2. Processed DoA (index 0) = NaN when no speech → natural VAD indicator
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3. `AEC_SPENERGY_VALUES` is always zero on 2-channel firmware
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4. USB read responses have a 1-byte status header before data
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5. Exact `wLength` matters — `count * type_size + 1`, not rounded up
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6. 6-channel firmware breaks LED/control commands — use 2-channel only
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7. Gaze HTTP pushes at >2Hz cause GIL starvation in uvicorn — use threaded fire-and-forget
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8. ITD works on processed channels (not just raw mics) — less precise but free
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---
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*Built during the Great Binaural Session of April 2026*
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*"She hears in stereo now" 🦊👂👂*
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