Whether you like it or not, AI generated music is getting good… like really good. In 2026, fully AI-generated tracks passed half of all the new music uploaded to Deezer each day, so it doesn’t look like AI music is going away any time soon.
So how do machines make music composition easier? How do they make it better? How do they produce music at all?
From Indy artists using AI for music videos, to AI cover song generators – In this article, we’ll take a closer look at the story of AI music. Where it’s at, how it’s used, and how you can get involved.
A Brief History of AI Generated Music
The idea of using artificial intelligence to generate music has been around for decades, with early experiments in algorithmic composition dating back to the 1950s and 60s.
Early AI compositions used rule based systems for basic music production. The computer was given a set of rules which it could follow to generate music.
Major breakthroughs came as neural networks and deep learning allowed artificial intelligence systems to analyze and learn from existing music data.
Companies like Amper Music and Aiva emerged, using AI to generate royalty-free music or even collaborating with human artists to compose pop songs.
Then things got wild. In 2020, OpenAI’s Jukebox generated raw audio, rudimentary singing included, in the style of different genres and artists. By 2023 and 2024, Suno and Udio could turn a typed prompt into a full song, vocals and all.
Today, AI music generators have become easily accessible for anyone who chooses to use them in their music production flow.
Novel AI tools such as AI cover song generators and music video apps are gaining popularity among creators on social media due to their unique and attention grabbing attributes.
How Does AI Music Work?
AI music works by training machine learning algorithms on existing musical data to generate new musical compositions and enhance existing ones.
AI music generators like Suno, Udio, Boomy and Soundraw all follow a similar pattern under the hood. Let’s take a closer look at the life cycle of AI generated music, from model training to the final human touches.
1. Data Collection
The first step in AI music generation is collecting large datasets for the AI music algorithms to learn from. The AI music generator needs access to thousands or even millions of examples of existing music compositions and audio across different genres like pop, rock, classical, jazz etc.
Typically this data is in the form of MIDI files, musical scores, or audio files like MP3s and WAVs. That data is what teaches the AI system about musical patterns, instruments, melodies, harmonies, rhythms, and other features and musical styles.
With enough data covering a fair chunk of existing human musical creativity, an AI music generator can build the foundations to create new AI music in any style. The quality and range of the training data directly impact how well the AI generated music will resonate with its audience.
As is always the case with AI – the more data, the better!
Where that data comes from is now the biggest fight in AI music. In June 2024, Universal, Sony and Warner sued Suno and Udio for training on their recordings without permission. Udio settled with Universal and Warner in late 2025, and Suno settled with Warner and launched its v6 models in September 2026, developed with Warner, BMG and Believe. Universal and Sony are still suing Suno, and filed a second lawsuit later that month. Meta’s open-source MusicGen, by comparison, was trained on 20,000 hours of licensed music.
2. Feature Extraction
Once an AI system has a large set of musical data, the next step to creating AI music is extracting meaningful features that represent different aspects of the compositions.
The raw audio is processed with digital signal processing (and, in newer systems, neural audio codecs that squash sound down into compact “tokens”) to pick out patterns like melodies, chords, rhythm, and instrumentation.
These musical structures are then translated into mathematical representations that machine learning algorithms can understand. For example, musical elements like pitch and rhythm can be encoded numerically as frequencies and durations.
The extracted features act as a distillation of the key elements. This allows the AI model to learn the statistical relationships between features, building an abstract understanding of how humans construct music, and what we like to hear.
3. Model Training
In model training, the machine learning algorithms are trained using the extracted musical features from the dataset.
Neural networks are commonly used, which learn by optimizing weighting parameters over many iterations. The goal is to build an AI music model that can estimate the probability of certain musical sequences occurring based on the patterns it observed in the training data.
Backpropagation does the heavy lifting here, nudging the model’s parameters after every guess so its output gets closer to the real music in the dataset. Reinforcement learning can then fine-tune a model based on feedback, like which versions listeners prefer.
After enough training on a broad dataset, the neural network builds an advanced understanding of musical theory and common practices, and the algorithm is ready to create its first piece of AI music.
4. Music Generation
So far, data has been collected, processed, and used to train a neural network. We’re now ready to use the model to create AI generated music.
How the music actually gets made depends on the model. Older symbolic systems write music note-by-note, chord-by-chord as MIDI or sheet music, sampling from their probability distributions to pick each next note.
Most modern text-to-music tools skip the sheet music and generate the audio itself. Meta’s MusicGen, for example, is a transformer model that predicts those compressed audio tokens one step at a time, a lot like a chatbot predicting the next word. Stable Audio takes a different route, using latent diffusion to sculpt a track out of noise, the same trick AI image generators use.
AI generated music allows for rapid creation of production-ready tunes in any style. The generated compositions often sound very good, while retaining the style and feeling of the existing music the AI analyzed during training.
If you’ve ever tried these tools, you might have been pleasantly surprised at how good the AI generated music can be.
5. Post-Processing
After AI music generators produce new compositions, post-processing is essential to refine the output for practical use in the music industry and elsewhere.
This phase often involves human intervention to enhance the AI-generated music, making it indistinguishable from existing music produced by human artists. Common tasks include mixing, mastering, and arranging, which elevate the quality of the AI-generated compositions to professional standards. Suno even has its own browser-based studio, Suno Studio, for editing AI tracks.
Post-processing serves as the final step to polish the AI generated music, ensuring that the output makes sense and is polished to a professional standard.
6. Iterative Improvement
In artificial intelligence music production, iterative improvement of the model plays a crucial role in advancing the quality of AI generated music.
By continually updating machine learning models with new music data and user feedback, the generated music continues to get better. This cycle of improvement ensures that each generation of AI assisted music production gets better at creating music we’ll actually listen to. Suno alone went from v3 in March 2024 to v6 in September 2026.
For music producers and the music industry at large, this means that AI-generated music is not some trend that will come and go, but an evolving tool that has real use cases in the music industry and music production process.
Tools to Get Started with AI Generated Music
Getting started with AI generated music is easier than ever. There is a range of readily available tools and applications you can play around with online to create AI music at a moment’s notice.
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Full Song Generation: Type a prompt (and your own lyrics, if you like) and tools like Suno or ElevenLabs’ Eleven Music write, sing and produce a whole track.
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Automated Mixing: Employ AI music tools to streamline the mixing and mastering phases, allowing people with little to no experience to create music that sounds professional.
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Synthetic Voices: Leverage machine learning technologies to produce synthetic voices that mimic specific artists, perfect for cover songs or collaboration concepts. Just know that Spotify only allows vocal impersonation when the artist has authorised it.
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Stem Separation: Utilize AI music tools to isolate tracks into individual stems, allowing for alterations or improvements.
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Concept Creation: Implement AI to craft prototype songs by blending your compositions with those of renowned artists, optimal for attracting interest from musicians or record labels.
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AI Music Videos: Explore AI solutions capable of generating music videos, offering time-saving advantages and engaging visual aesthetics to your audience.
As you can see AI generated music isn’t just limited to song generators. There are a range of ways musicians and creators can integrate AI into their music composition workflows!
Wrapping Up
AI music has moved from a fascinating experiment to a robust tool, fundamentally altering how the music industry approaches the creative process.
From data collection to post-processing, AI technology is streamlining workflows and enabling new music that stands toe-to-toe with human compositions.
As AI in music continues to evolve, the line between human and machine-made music is blurring, paving the way for a new era in the music industry.
Musicians now have the flexibility to focus on refining musical elements, and creating music that resonates with audiences while leveraging AI’s speed and efficiency. If you want to see how they’re doing it, check out the 6 ways creators use AI in music production and my roundup of AI music statistics.
The future of AI music is not just promising; it’s already here. Catch ya! 👋
