Entronaut: A Visual Explorer for Information Geometry

Turn complex data into visible patterns.

What is Entronaut?

Entronaut is a tool that transforms complex data—like audio signals, financial data, or text—into geometric shapes. Think of it like a mathematical prism: just as a prism splits a beam of light into a rainbow of colors, Entronaut splits a data stream into its fundamental patterns, making hidden structures visible and explorable.

This demo applies Entronaut to audio recordings of human and dog sounds to reveal their underlying mathematical similarities.

How is this different from tools like FFT?

Traditional tools like Fast Fourier Transform (FFT) or Wavelets are excellent for breaking down a signal into its basic frequencies. They can tell you what notes are in a piece of music, but not how they are arranged into a melody or harmony.

Entronaut goes a step further. It uses Information Geometry to analyze the relationships between data points. This allows it to capture not just the components, but the intricate structure of the data itself. It reveals the 'shape' of the information, showing you how patterns are organized and connected.

What is Information Geometry?

In simple terms, Information Geometry is a field of math that lets us measure the "distance" and "shape" of information.

Imagine you have two different news articles. You could count the words, but that wouldn't tell you how similar their meaning is. Information Geometry provides tools to define a meaningful distance between them based on the information they contain. Entronaut makes this concept visual, creating a 3D map where you can see how close or far different data points are in this abstract "information space".

This App's Goal: To demonstrate how Entronaut can map sounds from different sources into a shared geometric space. By exploring this space, you can see how Entronaut reveals underlying structural patterns in sound data.