Citizen Science: NoiseCapture App

Contemporary digital technology offers many opportunities for collecting scientific data. Millions of people are carrying highly capable networked computers (mobile phones), with cameras, microphones, and motion sensors. Most personal devices have capabilities available only in a few laboratories twenty years ago.

Furthermore, these devices are in the hands of “civilians”. It is now possible to do “citizen science” for real, using personal devices to collect data and aggregate it through network services.

This has been used for environmental sensing (microbe populationsmicrobe assays, weather, air pollution, particulates,, odors), earthquake detection, food quality, detecting poachers, and wildlife observations (pollinators.  bird watching, bird song, insect song).

As I have remarked before, simply collecting data is not actually that useful scientifically. It also invites misguided pseudoscicence, if data is not carefully analyzed or misinterpreted.

What is needed is the rest of the picture, including data cleaning, careful models and analysis, and useful , valid visualization and reports.  You know, the “science” part.

This summer, a team from several French research institutions are releasing the NoiseCapture app , which allows anyone tomeasure and share the noise environnement [sic]”.

Specifically, this app measures noise in a city, as the user moves through ordinary activities. The microphone records the sounds, and GPS tracks the local of the device. (There are plenty of tricky details, see their papers [1, 2].)

The collected data is transmitted to the project’s server, where it is analyzed and cross-calibrated with other data. Any given measurement isn’t terribly meaningful, but may data points from many phones combine to create a valid estimate of a noise event. They incorporate these data into a spatial model of the city, which creates an estimate of noise exposure throughout the area [1].

Ii is very important to note that estimating noise exposure from a mobile phone microphone is pretty complicated (see the papers). Crowdsourcing the data collection is vital, but the actual “science” part of the “citizen science” is done by experts.

I’m pleased to see that the researchers have done some careful development to make the “citizen” part work well. The system is designed to record readings along a path as you walk. The app gives visual indications of the readings and the rated hazard level that is being observed. The data is plotted on interactive digital maps so that many such paths can be seen for each city. The project also suggests organizing a “NoiseCapture Party” in a neighborhood, to gather a lot of data at the same time.

Overall, this is a well thought out, nicely implemented system, with a lot of attention to making the data collection easy for ordinary people, and making high quality results available to the public and policy makers.

This research is primarily motivated by a desire to implement noise control policies, which are written with detailed technical standards. Much of the work has been aimed to show that this crowdsourced consumer device approach can collect data that meets these technical standards.

That said, it should be noted that technical noise standards are not the same thing as the subjective comfort or nuisance value of an environment. One person’s dance party is another person’s aural torture. A moderately loud conversation might be unnoticed on a loud Saturday night, but the same chat might be very annoying on the following quiet Sunday morning.

I also have to say that I was a little disappointed that the “environment” in question is the urban streetscape. For instance, the app is not useful for indoors noise (where we spend a lot of time).

Also, I would love to have something like this to monitor the natural soundscape in town and country. When the machines and people aren’t making so much noise, there is still plenty to hear, and I would love to be able to chart that. These voices reveal the health of the wildlife, and it would be really cool to have a phone app for that.

This is what “dawn chorus” folks are doing, but they don’t have nearly as nice data analysis (and non Brits can’t get the app).

Finally, I’ll note that simply detecting and recording noise is only a first step.  In the event that the neighborhood is plagued by serious noise pollution, you’re going to need more than a mobile phone app to do something about it. You are going to need responsive and effective local and regional government.  There isn’t an app for that.

  1. Erwan Bocher, Gwendall Petit, Nicolas Fortin, Judicaël Picaut, Gwenaël Guillaume, and Sylvain Palominos, OnoM@p : a Spatial Data Infrastructure dedicated to noise monitoring based on volunteers measurements. PeerJ Preprints, 4:e2273v2, 2016/09/28 2016.
  2. Gwenaël Guillaume, Arnaud Can, Gwendall Petit, Nicolas Fortin, Sylvain Palominos, Benoit Gauvreau, Erwan Bocher, and Judicaël Picaut, Noise mapping based on participative measurements, in Noise Mapping. 2016.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s