What can AI do for the Music Industry?
Music artists, composers and producers today swim in massive amounts of musical notes to test the barriers of what melodies, harmonies and symphonies they can create and what works best with their songs. Although the advances in technology have significantly simplified and streamlined the process, it is still a long and challenging one for everyone involved in music creation.
However, a technological revolution may be about to chance music creation as we know it. A team of computer scientists were able to use AI to complete the unfinished 10th symphony, originally created over 250 years ago by Ludwig Van Beethoven. This project has provoked interesting discussions, such as whether the now completed symphony is what Beethoven was originally trying to create, and also raised the important question — what can Artificial Intelligence (AI) and Machine learning (ML) do for music production in the music entertainment industry?
The team at Brainpool have been pondering on the answer to the latter, so we took the time to test a few of the various readily available AI music demos and reflected on how they could help transform the music industry.
MusicVAE is a machine learning model, leveraging deep learning and neural networks, created by the Magenta team at Google’s AI division to help generate and create melodies for music composers and song writers through latent space interpolation. The team at Brainpool played around with Beat Blender, a free drum and beat demo tool created using MusicVAE’s deep learning models and neural networks to create a melody beat based on our manual input, and Melody Mixer, a tool that mixes tracks like Twinkle Twinkle Little Star with another to create a combined melody through MusicVAE’s deep learning models and neural networks. You can see the two in action below:
As we experimented, we adjusted the presets and found the tools adapted on the fly, allowing the drumbeat and melody to feel like a well-produced piece by top-of-the-line composers.
The intended target audience for this tool is a difficult question to answer. For actual music producers, this would be more of an accompanying tool for simplifying the process. But based on the technology already in use and the experience that these music producers have, adopting AI and ML may not be much of an improvement or a breakthrough, although it may certainly make the process more scalable via automation. The real benefit is for song writers. Most, if not all, song writers have very little experience in music production which makes the process of creating and publishing the song difficult. However, with MusicVAE, this can be considered a greater and more cost-effective tool for them to streamline the process, especially if they want to work in a small group or avoid large music producers and composers altogether. MusicVAE has the potential to help artists disrupt the traditional model of the music industry.
For the band YATCH, it allowed them to collaboratively work with MusicVAE and the team at Magenta to create a song that was out of their comfort zone yet also followed the parameters and preferences that they usually used to create previous songs.
