Talk2Nature

OUR APPROACH

Big curiosity.
Careful claims.

Progress starts by asking a question that can be answered — and being willing to hear an unexpected result.

Not every pattern
is a translation.

A recording can reveal identity, timing or context without revealing what an animal means. We use an evidence ladder to make those differences visible.

Our initial experiment will study observable context. Any later test of meaning needs receiver behavior and qualified scientific oversight.

  1. 01

    Detection

    A signal occurred.

  2. 02

    Identification

    A model or observer identifies the source.

  3. 03

    Association

    A signal is associated with an observed context.

  4. 04

    Meaning

    Controlled receiver responses support a bounded interpretation.

  5. 05

    Shared exchange

    A specific two-way interaction is demonstrated.

  6. 06

    General translation

    An ambitious goal, not a capability established by this project.

THE FIRST TECHNICAL QUESTION

Does sound add useful information?

For one chosen species, can acoustic features improve prediction of a pre-defined observable context on held-out recordings?

Observe independently

Label behavior from synchronized observations. Retain disagreement and unknowns; avoid deriving labels from the same sound a model sees.

Keep evaluation honest

Separate related recordings, individuals and sessions. Compare context-only, background and shuffled-label baselines.

Publish the limits

Report per-class errors, coverage, uncertainty and review effort. A failed hypothesis is still a useful result.

Open tools.
Responsible data.

Our original code is licensed under Apache-2.0. Original research summaries use CC BY 4.0. Recordings and model weights require their own rights checks.

Passive observation first

No automated playback in the initial public tools. No disturbing an animal to produce a label. A pilot needs a scientific collaborator, a stopping protocol and consent for any human data.

Start with a trustworthy manifest

The first code milestone validates recording metadata and keeps connected individuals, sessions and source hashes together when splitting data. Synthetic examples test the software; no animal model has been trained.

Help improve the method →