AI & ML
AircraftWitness: How Often Aircraft Can Locate Recordings
arXiv cs.SD0 views
arXiv:2611.08791v1 Announce Type: new Abstract: Aircraft overhead can reveal where an outdoor recording was made. Propellers and rotors leave Doppler curves in audio, and public ADS-B archives log equipped aircraft. We measure how often, blind and against chance. Our main test was registered and timestamped before any audio, and its two amendments came before any answer was read. It covers 24 new places, with the recorders' own clocks, a 300 s slack and a 20 km square. The best of three locators places clear-pass clips within 1 km at 4 places, where a uniform guess predicts 0.57 (p = 0.0065 after correcting for the pick). With the wrong time or swapped audio it places none. The other two hit 1 and 2. On 360 clips from 7 places of a GPS-timed network, two locators place 6% and 8% within 1 km. By place, a unit chosen after that run, they hit 4 and 5 of 7 where a uniform guess predicts 2.3. On 1,600 random public recordings, picked and cut without the truth in a time-bounded run, the three locators answer at most 20 times and place at most 2. We release AircraftWitness, a benchmark, and pass labels for 11.7 million recordings.
Source:https://arxiv.org/abs/2611.08791PreviousFoleyForge: Free-form, Controllable, Rectified-flow Editing of Sound EffectsNextFuzzy-FedAvg: Server-Aware Client Selection for Federated Speech Emotion Recognition in Heterogeneous Environments
Related posts
An Empirical Analysis of Static Analysis Methods for Detection and Mitigation of Code Library Hallucinations
0arXiv cs.SE
TATG: Tracking-Aware Testing Objective for LLM-based Test Generation
0arXiv cs.SE
MCP Server Architecture Patterns for LLM-Integrated Applications
0arXiv cs.SE
Introducing Cluster Efficiency to LLM Serving: Pooling Economical Accelerators over Fast Interconnects
0arXiv cs.DC
Evidence-Linked Radiology Reporting: A Human-Supervised Reference Architecture for Structured Imaging Intelligence
0arXiv cs.HC
Intentmaking and Sensemaking: Human Interaction with AI-Guided Mathematical Discovery
0arXiv cs.HC