Could you measure annoyance?
Today 55% of the world’s population lives in urban areas. People live in apartments and share spaces in close proximity. And it brings about interesting situations. One of them is a common experience of hearing sounds from neighbors and having no idea what is going on there. Oh, it could be super annoying. Our team decided to explore it further — could you design and train a home object to understand what annoys you specifically?
People want to feel that their homes are grounds for solace from a chaotic life outside, especially those living with roommates in large cities. What happens in reality, however, is that moments meant for peace and relaxation are rudely disrupted by all kinds of noises, often unpredictably. The mundane but disruptive noise nuisance leaves the urban dweller with little to do but to feel frustration in silence, and overtime grow resentment towards their neighbor.
Neighbor-ly concept
Neighbor-ly is a smart home object designed specifically to be in urban homes. It’s designed to address a problem that is all too common for people sharing walls with neighbors in city apartments — dealing with those obscure and obnoxious sounds that travel to your home in the most inconvenient of times. Is that someone on stilts walking around upstairs? Are they moving and dragging furniture all afternoon? Are those bags of marbles being dropped on the floor? What is that sound?
Neighbor-ly uses machine learning to be trained on picking up noises coming from neighbors, classifying them, and then reacting based on that classification. When it hears a sound, it also produces a phrase or a sentence guessing at the source of the sound — just like a person does when they hear something out of the blue. It learns over time which noises are classified as nuisances by the owner. The reaction from Neighbor-ly, a knock against a wall, occurs only after the object has been trained to understand and realize that this is a noise that the owner does not like. The knock is a signal to the neighbor. Quiet please!
Neighbor-ly externalizes an experience that often develops into an unspoken anger between neighbors. A loud party once in a while, which one might feel more comfortable confronting a neighbor about, is different than the more frequent and mundane interruptions that occur with more regularity. It leaves an urban dweller in discomfort without any action they can take to resolve the issue.
Through training and customization, Neighbor-ly allows the owner to feel like something is being done. The object is also a silent but proactive witness to the situation. In addition to that, it plays a role in adding humor to the experience to lighten the frustration for the owner by taking a guess at what the sound could be. It’s just as curious as you are about what that noise is!
black box
Neighbor-ly is powered by Arduino and the machine learning software Wekinator. A push button connected with Arduino allows the user to train the device by sending a signal to Wekinator via open sound control (OSC) to be classified. The system needs few samples of the sound to make better classifications and complete the training. In that way, a user creates their own library of sounds that are classified as “annoying”. When the system hears a sound, it runs it through the library. If a sound is classified as annoying, Wekinator sends a signal to Arduino to trigger the “broom”, activating a servo motor that controls the knocker.
The hardware consists of a WIFI enabled Arduino, a motor that moves the “broom”, a microphone that captures sounds and LCD screen that provides feedback to show the training progress as well as the humorous guesses about annoying sounds.
Critiques
Our exploration of Neighborly is a social commentary on how IoT devices will become more and more intrinsic to human life, and sneak in closer and closer to a person’s personal space — from wearables to homes. The prominence of ubiquitous devices may ‘solve’ for problems, however, the cost of those solutions may be that it breeds, or worse, amplifies, hostility or avoidance between people. “Solutions” like Neighbor-ly perhaps creates more distance and silos between human beings, while making people feel righteous in their silos.
With the presence of more and more customized devices and services, which learn the user’s behaviors, needs, desires, and wishes to the point of predicting those desires — the line gets blurry between a device correctly predicting what someone will want to do, to directing them to what they should do. As this becomes the normative behavior of humans and objects both — each depending on the other to tailor a human’s experience to specific exposures, the presence of spontaneity, discovery and serendipity from a person’s life is slowly eliminated.
You can find more about Neighbor-ly as an exploration of cramming in an IoT solution and details of the process on the Medium article.
Neighbor-ly was designed with irony in collaboration with Abhishek Kumar and Fahmida Azad at CIID under the mentoring of Massimo Banzi and Simone Rebaudengo.
Contribution: concept development, programming in Arduino, training in Wekinator and product design.
