A group of MIT neuroscientists developed a computer model that can recognize where a certain sound is coming from as well as the human brain. The capacity to locate and identify a sound and the source that emitted it in the first place is a complex ability performed by the human brain which has been a subject of persistent focus and research.
Akin to the human brain that takes into account the varied sound waves and measures the amplitude and frequency in question to assess sounds and differentiate them from each other, the model that aims to replicate the sound-identification ability of the human brain, also does so through a cumulative neural network in order to produce similar results, adapting and accommodating itself with the variations in the external environment.
The human brain is finely tuned not only to recognize particular sounds, but also to determine which direction they came from. By comparing differences in sounds that reach the right and left ear, the brain can estimate the location of a barking dog, wailing fire engine, or approaching car.
MIT neuroscientists have now developed a computer model that can also perform that complex task. The model, which consists of several convolutional neural networks, not only performs the task as well as humans do, it also struggles in the same ways that humans do.
“We now have a model that can actually localize sounds in the real world,” says Josh McDermott, an associate professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research. “And when we treated the model like a human experimental participant and simulated this large set of experiments that people had tested humans on in the past, what we found over and over again is it the model recapitulates the results that you see in humans.”
Findings from the new study also suggest that humans’ ability to perceive location is adapted to the specific challenges of our environment, says McDermott, who is also a member of MIT’s Center for Brains, Minds, and Machines.
McDermott is the senior author of the paper, which appears today in Nature Human Behavior. The paper’s lead author is MIT graduate student Andrew Francl.
When we hear a sound such as a train whistle, the sound waves reach our right and left ears at slightly different times and intensities, depending on what direction the sound is coming from. Parts of the midbrain are specialized to compare these slight differences to help estimate what direction the sound came from, a task also known as localization.
This task becomes markedly more difficult under real-world conditions — where the environment produces echoes and many sounds are heard at once.
Scientists have long sought to build computer models that can perform the same kind of calculations that the brain uses to localize sounds. These models sometimes work well in idealized settings with no background noise, but never in real-world environments, with their noises and echoes.
To develop a more sophisticated model of localization, the MIT team turned to convolutional neural networks. This kind of computer modeling has been used extensively to model the human visual system, and more recently, McDermott and other scientists have begun applying it to audition as well.
Convolutional neural networks can be designed with many different architectures, so to help them find the ones that would work best for localization, the MIT team used a supercomputer that allowed them to train and test about 1,500 different models. That search identified 10 that seemed the best-suited for localization, which the researchers further trained and used for all of their subsequent studies.
To train the models, the researchers created a virtual world in which they can control the size of the room and the reflection properties of the walls of the room. All of the sounds fed to the models originated from somewhere in one of these virtual rooms. The set of more than 400 training sounds included human voices, animal sounds, machine sounds such as car engines, and natural sounds such as thunder.
The researchers also ensured the model started with the same information provided by human ears. The outer ear, or pinna, has many folds that reflect sound, altering the frequencies that enter the ear, and these reflections vary depending on where the sound comes from. The researchers simulated this effect by running each sound through a specialized mathematical function before it went into the computer model.
“This allows us to give the model the same kind of information that a person would have,” Francl says.
After training the models, the researchers tested them in a real-world environment. They placed a mannequin with microphones in its ears in an actual room and played sounds from different directions, then fed those recordings into the models. The models performed very similarly to humans when asked to localize these sounds.
“Although the model was trained in a virtual world, when we evaluated it, it could localize sounds in the real world,” Francl says.
