An AI system for editing music in videos

Amateur and professional musicians alike may spend hours pouring over YouTube clips to figure out exactly how to play certain parts of their favorite songs. But what if there were a way to play a video and isolate the only instrument you wanted to hear?

That’s the outcome of a new AI project out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL): a deep-learning system that can look at a video of a musical performance, and isolate the sounds of specific instruments and make them louder or softer.

The system, which is “self-supervised,” doesn’t require any human annotations on what the instruments are or what they sound like.

Trained on over 60 hours of videos, the “PixelPlayer” system can view a never-before-seen musical performance, identify specific instruments at pixel level, and extract the sounds that are associated with those instruments.

For example, it can take a video of a tuba and a trumpet playing the “Super Mario Brothers” theme song, and separate out the soundwaves associated with each instrument.

The researchers say that the ability to change the volume of individual instruments means that in the future, systems like this could potentially help engineers improve the audio quality of old concert footage. You could even imagine producers taking specific instrument parts and previewing what they would sound like with other instruments (i.e. an electric guitar swapped in for an acoustic one).

In a new paper, the team demonstrated that PixelPlayer can identify the sounds of more than 20 commonly seen instruments. Lead author Hang Zhao says that the system would be able to identify many more instruments if it had more training data, though it still may have trouble handling subtle differences between subclasses of instruments (such as an alto sax versus a tenor).

Previous efforts to separate the sources of sound have focused exclusively on audio, which often requires extensive human labeling.