Of all the areas that AI is influencing these days, music production might seem like an unlikely one. While there are certainly many technical aspects to producing music, coming up with material that really speaks to people seems like something that must be primarily driven by humans.
Not necessarily, though. Actually, AI music is taking off big time this year, especially with advanced programs like MuzMaker. Let’s take a closer look at what is behind these advancements, and where they seem to be going.
Huge progress in a short period of time
It seems like even just recently, the idea of AI music went from being a passing fancy to something that people really take seriously. And tools today can accomplish a whole range of things, from composition to editing, mixing and more.
Composition tools
The most fundamental aspect of production is the ability to compose. Several new products are on the market these days that break down the composition process and produce AI-generated content. This involves:
- Deep analysis. Once an AI model is created, it analyzes thousands of hours of music to dissect the music’s logic, including chord succession and how different instruments work together.
- Prompts elicit content. When users input prompts, such as genre, instrument type, and mood, tools can come up with appropriate content accordingly.
- Algorithms that mimic language models. Many follow similar models to those used in language generation. Content also applies to melodies and harmonies.
Song generation tools
Beyond instrumental composition, AI tools are able to produce whole songs that include lyrics. Tools utilize specific mechanisms:
- GPT and related models to create lyrics. GPT stands for generative pre-trained transformer, and it is a type of large language model. GPT models can be used to generate lyrics that rhyme, follow a particular theme, or otherwise have a poetic flow to them. Programs can then turn these lyrics into verses, choruses, and bridges.
- Text-to-audio models. These models use deep learning to convert prompts into audio by mapping the prompts onto different sound patterns. They might use latent diffusion models to come up with segments that have particular styles or rhythms.
- Tools that provide structure. Sometimes users have content in mind, but need help structuring it. There are tools that can take prompts such as “intro,” “verse,” etc and organize content into appropriate parts and order.
Some tools include features such as in-browser editing, which allows for personalized structuring. Others will allow you to work on only select pieces of songs, if this is what is called for. One app allows for music creation based upon the natural flow of conversation, using voices as a guide.
Apps that master and mix
There are also apps that will take completed music and provide finishing touches to it, or remix it altogether. They can accomplish very interesting things:
- In mastering, apps utilize dynamic EQ to “listen” and adjust frequency so that the music will be optimized across devices.
- AI can optimize volume by utilizing limiters and compressors so that tracks can reach commercial volume standards without issues.
- Apps can also enhance stereo fields so that tracks can reach much larger spaces, allowing sound to fill out rooms and make the listening experience much more intense.
- Some apps can optimize for specific genres as they are programmed to detect precisely what parameters are necessary for different types of music.
Apps that edit
The ability to edit music can be just as important as the ability to create it from scratch. There are tools available that can perform critical editing functions for existing compositions:
- Apps can perform stem editing by performing spectral analyses and converting audio waves into a 2D spectrogram, thus enabling the analysis of different musical patterns.
- They can perform mask estimation, which creates filters for specific instruments, allowing the spectrogram to determine which parts should be kept and which discarded.
- AI can also repair audio in very granular ways, including context-aware denoising. If an AI model is programmed to distinguish between background noise and primary audio, it can perform the function of suppressing that, which is undesirable and leaving the rest.
- Some apps can also perform “spectral repair,” which uses AI to fill in missing frequency content that is sometimes caused by older music.
- There are tools to restore voice or instruments that need to be brought out more. These tools can do things like remove echoes, reconstruct damaged recordings, and rescue what would otherwise be considered unusable audio.
Things to keep in mind
There is no question that AI has great things to offer in the area of music production. There are, though, some ethical and legal things to keep in mind as the industry grows. One has to do with the commercial rights of creators. Companies need to keep the question of legitimate royalties in mind and be sure not to infringe upon copyrights. This is becoming an increasingly fuzzy issue as time goes on.
Another major consideration is the human element. No matter how sophisticated AI tools might get, they will never fully replace humans in the creation process. The people behind AI companies need to keep this in mind and be sure not to stray too far from the real human talent involved in music creation.
Check out available online tools
The future of AI music production is bright, indeed. If you have an interest in this area–be it casual or serious–get online and check out what is available. You will likely be amazed at the things that these programs can do. And they could open up a whole new world for you.
