Music Generation AI Models
AI music generation models are transforming production by simplifying sound creation. The market is projected to reach $2.8 billion by 2031, enhancing rather than replacing traditional artistry and fostering appreciation for live music.
Read original articleThe article discusses the emergence of AI music generation models and their impact on music production. A new project, text-to-sample.com, allows users to generate sounds by describing them in text prompts, streamlining the process for music producers who traditionally rely on sample packs. The author outlines the evolution of music technology through four eras: pre-electric, analog/electric, digital, and the current AI era, which is characterized by machine learning models that simplify music creation. Various AI tools are highlighted, including those for full song generation, stem splitting, vocal synthesis, and mastering. The author predicts significant growth in the AI music market, estimating it will reach $2.8 billion by 2031. While AI tools may replace some functional music roles, the author believes they will not replace artists but rather augment their creative processes. The article concludes with a hope that the rise of AI-generated music will lead to a renewed appreciation for authentic, live music.
- AI music generation models are transforming music production by simplifying sound creation.
- The AI music market is projected to grow significantly, reaching $2.8 billion by 2031.
- Various AI tools are available for tasks like song generation, vocal synthesis, and mastering.
- The author believes AI will enhance rather than replace traditional music artistry.
- There is hope that AI's rise will foster a greater appreciation for live music.
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> Vocal Synthesis: This allows one to generate new audio that sounds like someone singing. One can write lyrics, as well as melody, and have the AI generate an audio that can match it. You could even specify how you want the voice to sound like. Google has also presented models capable of vocal synthesis, such as googlesingsong.
Google's singsong paper does the exact opposite. Given human vocals, it produces an musical accompaniment.
In 2019, I built this thing called RaveForce [github.com/chaosprint/RaveForce]. It was a fun project.
Back then, GANsynth was a big deal, looked amazing. But the sound quality… felt a bit lossy, you know? And MIDI generation, well, didn't really feel like "music generation" to me.
Now, I'm thinking about these things differently. Maybe the sound quality thing is like MP3 at first, then it becomes "good enough" – like a "retina moment" for audio? Diffusion models seem to be pushing this idea too. And MIDI, if used the right way, could be a really powerful tool.
Vocals synthesis and conversion are super cool. Feels like plugins, but next level. Really useful.
But what I really want to see is AI understanding music from the ground up. Like, a robot learning how synth parameters work. Then we can do 8bit music like the DRL breakthrough. Not just training on tons of copyrighted music, making variations, and selling it, which is very cheap.
IMO this would be much more useful.
[0] https://suno.com/song/0caf26e0-073e-4480-91c4-71ae79ec0497
> Stem Splitting: This allows one to take an existing song, and split the audio into distinct tracks, such as vocals, guitar, drums and bass. Demucs by Meta is an AI model for stem splitting.
+1 for Demucs (free and open source).
Our band went back and used Demucs-GUI on a bunch of our really old pre-DAW stuff - all we had was the final WAVs and it did a really good job splitting out drums, piano, bass, vocals, etc. with the htdemucs_6s model. There was some slight bleed between some of the stems but other than that it was seamless.
If this happens, main character syndrome may get a bit worse :)
AI models are tools, and engineers and artists should use them to do more per unit time.
Text prompted final results are lame and boring, but complex workflows orchestrated by domain practitioners are incredible.
We're entering an era where small teams will have big reach. Small studio movies will rival Pixar, electronic musicians will be able to conquer any genre, and indie game studios will take on AAA game releases.
The problem will be discovery. There will be a long tail of content that caters to diverse audiences, but not everyone will make it.
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