Simon Willison November 3, 2026

I discovered a sound that leaks your exact location (and it's everywhere) - YouTube

The article describes how an individual discovered that a subtle background sound in a YouTube video can reveal the viewer’s exact location. By extracting faint plane noises from the soundtrack, analyzing their Doppler shifts, and matching these patterns with ADS‑B flight data, the author can pinpoint the video’s geographic origin based on aircraft speed, distance, and flight gaps. This method demonstrates a novel privacy vulnerability in seemingly innocuous media.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Simon Willison.

safety

arXiv AI

Jun 15

Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources

arXiv:2606. 14141v1 Announce Type: cross Abstract: Sound events are entities with semantic identities, locations, and trajectories, but current audio-language models usually reason about clips as global event content.

By Oh Hyun-Bin, Kazuki Shimada, Yuhta Takida, Kim Sung-Bin, Toshimitsu Uesaka, Takashi Shibuya, Kyeongyoon Lee, Tae-Hyun Oh, Yuki Mitsufuji

llms benchmarks

arXiv Machine Learning

Jun 19

Bioacoustic Geolocation: Species Sounds as Geographic Signals

arXiv:2505. 18726v3 Announce Type: replace-cross Abstract: Can we determine someone's geographic location solely from the sounds they hear?

By Mustafa Chasmai, Wuao Liu, Subhransu Maji, Grant Van Horn

multimodal benchmarks

arXiv AI

Oct 2

Supervising Sound Localization by In-the-wild Egomotion

The paper introduces a method for learning binaural sound localization by using egomotion as a supervisory signal. By tracking how a camera’s direction changes relative to a sound source during a video, the authors train an audio model to predict sound directions that align with visual estimates of camera motion derived from multi‑view geometry. They evaluate this approach on a newly proposed dataset of real‑world audio‑visual videos with egomotion, demonstrating that the model can learn from real data and perform well on sound localization tasks.

By Anna Min, Ziyang Chen, Hang Zhao, Andrew Owens

multimodal

arXiv AI

Jul 21

RealDESED: A Real-World Domestic Sound Event Detection Benchmark

arXiv:2607. 16736v1 Announce Type: cross Abstract: This paper presents RealDESED, a real-world domestic sound event detection (SED) benchmark comprising 5,710 audio recordings collected by 652 participants in their homes.

By Florian Schmid, Paul Primus, Alexander Fichtinger, Tara Jadidi, Tobias Morocutti, Gerhard Widmer

llms benchmarks

arXiv Machine Learning

Aug 17

SALSA-V: Shortcut-Augmented Long-form Synchronized Audio from Videos

arXiv:2510. 02916v2 Announce Type: replace-cross Abstract: We propose SALSA-V, a multimodal video-to-audio generation model capable of synthesizing highly synchronized, high-fidelity long-form audio from silent video content.

By Amir Dellali, Luca A. Lanzend\"orfer, Florian Gr\"otschla, Roger Wattenhofer

diffusion fine-tuning multimodal benchmarks safety

arXiv AI

Aug 18

Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task

arXiv:2608. 14916v1 Announce Type: cross Abstract: AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or otherwise edited.

By Alexandru-Stefan Morosanu, Valerian Cecan, Stefan-Daniel Achirei, Laura Erhan

llms

One email a morning, machine-written

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Spatio-Temporal Audio Language Modeling for Dynamic Sound Sources

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Supervising Sound Localization by In-the-wild Egomotion

RealDESED: A Real-World Domestic Sound Event Detection Benchmark

SALSA-V: Shortcut-Augmented Long-form Synchronized Audio from Videos

Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task