Modulate Fingerprint

The Modulate fingerprint is the house data visualization for conversations. The X axis is a timeline that operates on fragments of speech — clips — rather than raw audio; the Y axis discretely splits the picture by speaker, one lane per voice. The standard encoding is color for detected emotions and a glyph for detected behaviors.

The base format

The base format flexes within fixed ranges. Every picture below is the same component — only the settings differ. The fingerprint can be combined with the player: it is the player's data visualization.

Or it can stand alone, as pure data.

The number of speakers is 1, 2 or N — each voice gets its own lane. A single speaker reads as one strip:

A full room splits the same height into lanes:

The displayed information is a dial too. Bare clips only mark who spoke and when:

Detected emotions add color, and detected behaviors add a glyph on top:

At the icon scale the language compresses to a few bars. These are actual design-system icons:

Between the icon and the full view live miniatures — fingerprints in a table row, next to the conversations they belong to:

Or a plain flow of conversations, wrapping like text:

Derived formats

Derived formats appear when the rules bend further.

Color can encode another data layer instead of emotions — synthetic voice or AI music detection. The segmentation changes with it: equal windows instead of utterances.

One speaker's clips can be glued together into a speak-time bar: the width is their share of the conversation.

And the Y axis can double as amplitude, imitating a waveform.

It is important to understand that these derived formats cannot serve as the basis of recognizable brand imagery. They exist to solve specific tasks — when an idea tied to a particular model needs to be visualized, and needs to be visualized the Modulate way.