Sound Has Context

Sociability collects accessibility information across a wide range of factors, from mobility measurements to visual, hearing and sensory conditions such as colours, smells and sounds. During fieldwork with users and venue owners, a recurring issue emerged: the app's sound data was too simplistic to be useful.

Sound was represented with a binary label: Quiet / Loud. But sound environments are highly contextual. A café might be calm in the morning and overwhelming during peak hours. For users with sensory sensitivities, hearing conditions or anxiety, this difference can determine whether a place feels accessible or not.

The current model created two problems:

This revealed a deeper product challenge: Sociability's mission is not only to provide that information, but to also make the data collection process itself inclusive and participatory, allowing users to shape the database by contributing their own experiences. There was an opportunity to improve the experience as a whole.

To understand the problem more deeply, I built on existing research conducted with disability organisations and app users, which mapped the types of sound environments most relevant to different user groups.

I then conducted additional in-field sessions with users and business owners, observing how people navigated venues and how sound conditions varied across time and context.

Alongside this, I ran:

This allowed me to understand both user needs and product constraints, including how data was collected, structured and displayed in the app.

Mapping the existing system revealed that the problem was not only about sound labels, but about the entire data collection workflow.

Problems identified

Opportunities

The challenge became an opportunity to rethink how sensory accessibility data is structured and collected. Key directions included:

Based on these insights, I proposed a redesigned sound data system built around two complementary layers.

1. Objective sound measurement

Users could record sound levels using a standardised decibel scale, visualised with a colour-coded spectrum. This introduced a shared reference point across users and venues, reducing subjective interpretation and increasing confidence in the data.

2. Contextual sound descriptors

Alongside measurement, contributors could classify the type of sound present, using an expanded taxonomy based on earlier user research. This allowed the system to capture both intensity and character, giving users richer information about the environment.

Sound accessibility data combines:

This hybrid model allows the system to represent environments more faithfully than binary labels, giving users the nuance they need to make confident decisions.

Beyond the data model itself, the proposal redesigned the entire sound contribution experience. The goal was to make the act of contributing more inclusive, structured and trustworthy.

Key improvements included:

Although the feature did not ship before I left Sociability, the proposal was delivered to the product and design team and received positively as a direction for improving sensory data across the platform.

The project demonstrated how small data model decisions can significantly influence trust and usability in accessibility technologies. It also deepened my understanding of the challenges of translating complex, subjective human experiences — such as sound perception — into digital systems that remain both reliable and inclusive.