The Ambient Sound Map

Oto Fūkei - The Interactive Ambient Sound Map

Oto fūkei (音風景) is a Japanese term meaning “soundscape” or “sound landscape”. It is the idea that places are not only defined by what they look like, but also by the sounds that shape our emotional perception of them. A narrow Tokyo alley has a very different sonic identity from a riverside park. A dense commercial district feels different from a suburban rail corridor.

Inspired by the idea of oto fūkei, I’ve been experimenting with an interactive map that generates ambient soundscapes directly from OpenStreetMap data. As you pan around the Oto Fūkei map, the visible roads, buildings, parks, waterways and rail infrastructure are analysed in real time to create a dynamic ambient soundtrack that reflects the character and atmosphere of each neighborhood.

Using MapLibre GL JS, the map continuously analyses the visible vector tile features in the current viewport, including:

• roads
• buildings
• parks and greenery
• water
• rail infrastructure

These features are used to create a dynamic perceptual mix describing the environmental character of the current map view.

For example:

• roads increase traffic intensity
• greenery increases nature ambience
• water adds softer aquatic textures
• railways introduce transit sounds

As you pan around the map, the perceptual mix changes in real time and the ambient audio changes with it.

The current prototype uses layered environmental recordings from the BBC Sound Effects archive, blending traffic, urban ambience, nature, water and transit sounds to simulate the sonic atmosphere of different locations.

The aim isn’t strict realism, but to explore how geographic data can be translated into mood, atmosphere and environmental identity. At the moment the Oto Fūkei map is still a fairly crude simulator of ambient sound. The current prototype only analyses five broad environmental factors (roads, buildings, greenery, water and rail infrastructure) and generates its soundscape using only five layered ambient recordings. 

Future versions of the map could use a much richer environmental model, incorporating factors such as land use, population density, weather, time of day and live transport data, alongside larger libraries of spatial and procedural audio.