Artificial Intelligence Trained To Recognize Different Sounds By Binge Watching

Although several examples of artificial intelligence have gotten extremely adept at recognizing speech and voice patterns such as Siri and Cortana, recognizing other sounds has been a significant hurdle for machines. However, it would seem that MIT engineers finally managed to overcome this hurdle by teaching an AI how to recognize specific sounds and even identify the environments featured.

Some examples of sounds that AIs have had trouble dealing with in the past include crashing waves, a cheering crowd, indoor chatter, and even crying children, Phys.org reports. By allowing the AI to learn how to recognize which sounds come from what source via hours of watching videos, however, researchers from the Computer Science and Artificial Intelligence Laboratory at MIT created a sound-recognition system that is able to overcome this obstacle.

Basically, the researchers allowed the AI to watch scenes and settings in order to categorize them. They then used a second set of machines to discover the corresponding sounds to the videos. As Carl Vondrick, one of the authors of the study said, advancements in computer vision led to this breakthrough.

"Computer vision has gotten so good that we can transfer it to other domains," Vondrick said. "We're capitalizing on the natural synchronization between vision and sound. We scale up with tons of unlabeled video to learn to understand sound."

Another example of AI being able to recognize natural sounds is the work of Manny Tan, Kyle McDonald, and Jessie Barry. Both Tan and McDonald are programmers, while Barry is an ornithologist. By combining their expertise, they were able to teach an AI to catalog the different sounds that bird species make, and even generate visual elements in the process, Futurism reports.

The algorithm used basically produced the specific fingerprint of each specific bird based on the sounds they made using the “t-distributed stochastic neighbor embedding” machine learning technique. The results of the collaboration essentially opened the floodgates to new applications in various fields for detection and manipulation.