Does a longer window, a lower gamma or a delay prior fix the period aliasing and early-start failures of GCC audio sync on real clips?
research/capture-aliaswin
Does a longer window or a delay prior fix GCC sync's aliasing on real clips? Window length (shared audio) is the lever: 97% wrong at 1 s, 38% at 2 s, 16% at 3 s on the failing clips; a tone clip needs a prior tighter than its period.
What it found
Follow-up to capture-residgate section D. On ten real LibreOffice gallery clips (0 dB white noise, 0.8 s offset, 432 windows per clip and cell), the share of delay estimates over 2 ms off on the four failing clips falls from 97% at a 1 s window to 38% at 2 s and 16% at 3 s (gamma 0.25), because two cameras with a 0.8 s offset share only W−0.8 s of audio. Gamma moves 2 s results between 38% and 76% (PHAT worst). A tone clip (space2) is not helped by more audio (52–70% aliased at 2 and 3 s); only a delay prior tighter than its 12 ms period works (oracle ±10 ms: 0% wrong; ±50 ms: 65% still aliased). The early-start failures of untie and soft (59–77% wrong when the window starts in the clip's first half second, 0% later) are partly a short shared segment; the shared audio is only 2–5 dB quieter in wrong windows, so loudness is a weak explanation. Limits: ten clips, white noise at one SNR, 3 s windows barely vary their start (one draw of content per clip), the prior is an oracle ceiling.
Paper, code and results
More in Autonomous multi-angle capture
- How long a window of sound does it take to sync two drifting cameras, and how often must you redo it?How long a window of sound syncs two drifting cameras? GCC-PHAT plus a robust line fit syncs to under 0.02 ms over an hour from -10 dB SNR with 2 s windows; a sync made once at the start drifts one frame in 833 s at 40 ppm.
- How small can a face or a person be before a real detector stops finding it?How small can a face be before a real detector misses it? YuNet found every face of 16 px or wider and half at 10 px (assumed: 20), cutting the modelled blur leak on an 8K 360 camera from 4.8% to 0.01%; YOLOX finds half of people at 23 px tall. A pilot on 22 easy faces in 128 COCO photos: an optimistic bound.
- Does a predictive gap-seeking planner recover the framing a follow camera loses when it avoids pedestrians?Does a predictive planner recover the framing a follow camera loses by avoiding pedestrians? Not at equal settings (drone 58% framed vs 68% for repulsion at 0.1 people/m²), but it halves-to-tenths the contacts (36 vs 161 per hour; wheeled 21 vs 326) and moves the framing/contacts front left. 2-D simulation, assumed platform figures.
- How much of a predictive follow camera's contact reduction survives noisy, delayed pedestrian detections?How much of a predictive follow camera's contact reduction survives noisy, delayed pedestrian detections? Framing is unchanged (57-61%) but a drone's contacts rise from 36 per hour with exact data to 120 at 0.3 m noise and 0.25 s delay, and 373 at 1 m and 1 s (ignoring pedestrians: 523). 2-D simulation, swept noise, perfect association assumed.
- What does it cost a travel filming kit to also carry its footage backup?What does it cost a travel filming kit to carry its footage backup? Counting storage costs 3 of 18 shots at 4 kg; the lightest kit reaching all 18 grows from 3,900 g to 4,125 g.
- Can a small model trained on many random scenes choose the per-pixel blend weight for stereo-depth view synthesis better than a hand-set ramp?Can a small model trained on many random scenes pick the per-pixel blend weight better than a hand-set ramp? A one-hidden-layer network beats the best soft gate by 0.57 dB on 40 held-out scenes (all 40 ahead) and recovers 0.36 of the headroom to the best per-pixel blend.
All 34 Autonomous multi-angle capture projects →
Other research topics
Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.