Creating Music with AI: A Complete Beginner's Guide
- Stéphane Guy
- Feb 12
- 12 min read
AI-assisted music creation is no longer just a technological curiosity. Today, it's transforming how both amateur and professional musicians compose, produce, and experiment. Where mastering an instrument, understanding music theory, and/or investing in expensive equipment was once required, a few text instructions are now enough to generate a complete song. This democratization raises as much enthusiasm as it does questions: How far can you go without musical knowledge? Do these tools really replace human creativity? And above all, how do you actually get started as a beginner? This guide takes you step by step through the world of AI music composition.
In short
Accessible tools without musical skills: AI generators allow you to create complete tracks from simple text descriptions, with no prior training required.
Multiple approaches available: Automatic generation of complete songs, creation of instrumentals, enhancement of existing compositions, or assisted production for experienced musicians.
Free or paid depending on your needs: Most platforms offer limited free versions, with subscriptions typically starting around $10/month.
Professional quality achievable: Recent models produce surprisingly convincing results, sometimes indistinguishable from human productions in certain genres.
Legal questions still unclear: Intellectual property, copyright, and commercial use vary by platform and remain a shifting landscape.
Why AI Music Really Changes the Game
We've seen countless technological revolutions announced in music. From analog synthesizers to digital audio workstations, each innovation has redefined creative possibilities. Artificial intelligence follows in this lineage, but with a key distinction: it doesn't just add new tools : it modifies the very nature of the creative process.
For the first time, creating music no longer requires prior technical skills. A marketer can compose the jingle for their ad campaign. A teacher can musically illustrate their lessons. A content creator can produce custom soundtracks for their videos. This accessibility disrupts established norms.
Taryn Southern, an American artist and pioneer of AI music, released the album "Break Free" in 2017, composed using artificial intelligence tools. In an interview with The Verge, she explained that she knew "very little about music theory." She didn't know "how to play what I was hearing in my head," hence her use of artificial intelligence to compose her music.*
This statement perfectly illustrates the paradigm shift: AI becomes a creative collaborator that compensates for technical gaps while preserving artistic intent.
But beware of illusions. While these tools democratize creation, they don't yet replace artistic judgment, the ability to structure a piece, or refine a production. They simply shift the necessary skills: less instrumental technique, more creative direction and iteration.
Want to form your own opinion? We invite you to discover AI-created music on our 360°IA website!
Different Ways to Create with AI Music
Contrary to popular belief, AI music creation isn't just about generating random songs. Several approaches coexist, each serving different needs.
Complete Song Generation
This is the most spectacular approach and the one that regularly goes viral on social media. Platforms like Suno AI or Udio can create complete tracks with vocals, instruments, and structure, solely from a text description.
You write "upbeat pop song about summer vacation, cheerful female vocals, acoustic guitar and light percussion," and the AI generates a multi-minute track in seconds. The results can be surprisingly convincing, especially in genres like pop, alternative rock, or electronic music.
Users retain the ability to refine their request, regenerate certain sections, or combine multiple generations. This is particularly suited for content creators who need customized musical illustrations quickly.es.
Instrumental and Loop Creation
For those who only want a musical base without lyrics, other tools specialize in generating instrumentals. Soundraw or Soundful offer the creation of customized musical atmospheres by genre, tempo, emotion, and instruments.
These platforms primarily target videographers, podcasters, or video game developers looking for royalty-free, adaptable soundtracks. The interface typically allows you to modify the intensity of different passages, adjust the structure, or regenerate certain sections.
The advantage? Significant flexibility and exports in various formats suitable for audiovisual production. The downside? A certain uniformity in highly requested genres.
Composition Assistance for Musicians
Experienced musicians find value in more specialized tools that assist without replacing. AIVA (Artificial Intelligence Virtual Artist) or Amadeus Code generate chord progressions, melodies, or arrangements that composers can then refine in their preferred production software.
Magenta Studio, developed by Google, offers plugins for Ableton Live that generate melodic variations, harmonic continuations, or rhythms based on what you play. AI becomes a creative partner here, suggesting directions you might not have considered.
This collaborative approach preserves artistic control while exploiting AI's ability to rapidly explore thousands of musical possibilities.
Assisted Production and Mastering
Beyond pure composition, AI is also investing in production and finalization stages. LANDR, iZotope Ozone, or eMastered analyze your tracks and automatically apply compression, equalization, and mastering adapted to the musical genre.
These services make accessible a stage once reserved for professional studios. For a lower cost, you get acceptable quality mastering, though expert ears can still distinguish the difference from personalized human work.
How to Get Started Concretely: A Step-by-Step Journey
Let's get practical. You've never touched these tools and want to create your first track? Here's a step-by-step guide to better understand how to proceed.
Step 1: Choose the Right Platform Based on Your Goal
Don't dive in randomly. Your choice depends on what you want to achieve and your level of requirement. For a quick and free first try, Suno AI remains hard to beat. The interface is intuitive, results come quickly, and the free version allows you to generate several tracks per day.
If you're aiming for instrumentals for your YouTube videos, Soundraw or Beatoven.ai offer even simpler interfaces, with sliders to adjust atmosphere, tempo, and intensity.
Musicians wanting more technical control will turn to AIVA or Boomy, which offer more parameters but require more learning time.
Step 2: Formulate an Effective Request
This is where everything happens. A vague description will give generic results. An overly complex instruction risks confusing the AI. Balance lies in targeted precision.
Rather than writing "a beautiful song," prefer: "Acoustic folk ballad, melancholic male vocals, dry guitar and harmonica, desert road atmosphere, slow tempo at 70 BPM."
Elements to systematically specify:
Musical genre (be specific: not just "rock" but "90s alternative rock Radiohead-style")
Main instrumentation (acoustic guitar, piano, synthesizers, electronic drums...)
Vocal character (male/female voice, deep/high timbre, melodic/rap style...)
Emotional atmosphere (joyful, nostalgic, energetic, contemplative...)
Cultural or artistic references (optional but sometimes effective)
Don't write your prompts in French if you're using international platforms. English generally gives better results, as models are primarily trained on English-language data.
The way you create the "prompt" isn't trivial, and shows that the more musical knowledge you have and/or a precise idea of what you want to create, the more effective the result will be. Indeed, providing precise data to the AI like notions of tempo, vocalization, BPM, etc., will allow it to better understand your request, rather than simply saying "fast rhythm."
However, the result may not meet expectations, as each musical generation differs from the previous one, and you'll sometimes need to generate the same prompt several times to find a result that suits you.
This confirms an approach to AI music creation: the more skills you have in the field, the more effective the result will be. Without quality instructions, AI can't work miracles. It's therefore an aid for specialists and an accelerator for beginners, not a long-term substitute.
Step 3: Iterate Without Getting Discouraged
Your first generation will rarely be satisfactory. This is normal and doesn't question your ability to use the tool. AI music works through trial and error, like digital photography or video editing.
Regenerate several versions with the same instruction. You'll notice significant, sometimes surprising variations. Then, gradually refine your prompt based on what works or doesn't.
If the voice doesn't suit you, specify the desired timbre. If the instrumentation is too loaded, explicitly request "minimalist arrangement." If the rhythm seems off, indicate the exact tempo in BPM.
Some platforms allow you to regenerate only the intro, chorus, or bridge. Exploit this feature rather than systematically starting over.
Step 4: Exploit and Personalize the Result
Once your track is generated, several options are available depending on your technical level and goals.
The simplest use: download the audio file and integrate it directly into your project (video, podcast, presentation...). Most platforms offer MP3 or WAV exports of decent quality.
To go further, import the file into free audio editing software like Audacity or GarageBand.
You can then:
Cut certain sections to create a perfect loop
Adjust sound levels between different parts
Add effects (reverb, echo, filters...)
Combine several different AI generations
The most motivated can even extract stems (separate tracks for vocals, drums, bass, etc.) when the platform allows it, and rework each element individually in a digital audio workstation (DAW) like Ableton Live or FL Studio.
This hybrid approach, mixing AI generation and human editing, often produces the most interesting and personalized results.
Step 5: Understand Your Rights and Usage Limitations
Before publishing or commercializing your creation, absolutely verify the platform's terms of use. It's much less glamorous than creation, but it avoids legal problems later.
Suno AI, for example, specifies in its terms that free version users don't own commercial rights to their creations. Only paying subscribers can monetize their tracks (and only newly created music—rights aren't retroactive!).
Other platforms like Soundraw grant usage licenses even on free plans, but with restrictions on monthly downloads or publication and sound usage methods.
The question of intellectual property remains complex. In Europe, the legal situation remains unclear and will likely evolve with ongoing regulations. When in doubt, consider your AI creations as working tools rather than final protectable works.
Current Limitations to Know in AI Music Creation
Let's be frank: AI music creation is impressive, but it's not magic. Several constraints persist and determine what you can reasonably expect.
Narrative and Emotional Coherence of AI-Generated Music
Current AIs excel at reproducing musical patterns but struggle to build subtle emotional progression over the duration of a track. A bridge that should create rising tension sometimes arrives too early or too late. A chorus that should explode sounds strangely restrained.
This limitation stems from the very functioning of generative models: they locally predict what should follow, without an overall vision of the track's narrative arc. A human composer thinks structure and climax. AI juggles sequential probabilities.
Sometimes Incoherent Lyrics in AI Music
If you generate a song with lyrics, expect surprises. Sure, the AI aligns words that rhyme and fit correctly into the melody. But the overall meaning often wavers. Haphazard metaphors, abrupt subject changes, awkward repetitions.
In genres where lyrics matter less (electronic, ambient music, instrumental), this isn't a problem. In a folk ballad or rap, it's immediately noticeable. Some users work around the problem by providing their own lyrics and requesting only the musical composition.
To address this, don't hesitate to directly provide your own lyrics when the tool allows it. For example, you can do as we do at 360°IA: create your lyrics via generative AI like ChatGPT or Gemini, then use this creation in music generators!
Residual Sound Artifacts
Listen carefully to your generations with headphones. You'll sometimes notice fleeting crackles, imperfect transitions between sections, strange resonances on certain instruments. These artifacts, though less frequent than two years ago, haven't disappeared.
Recent models like Suno v4 or Udio v1.5 have considerably reduced these defects, but they resurface especially on complex requests with many instruments or abrupt atmosphere changes.
Difficulty Exactly Reproducing an Idea
Unlike traditional composition software where you place each note precisely where you want it, generative AI remains partially unpredictable. You can precisely describe what you want, regenerate twenty times, and never get exactly what you had in mind.
This uncertainty is part of the process. Some find it frustrating. Others appreciate it as a source of creative serendipity: happy accidents they wouldn't have imagined alone.
Legal and Ethical Questions to Keep in Mind
Beyond purely technical aspects, creating music with AI raises deeper questions worth examining.
Model Training on Existing Works
All current music generators have been trained on immense catalogs of existing songs. Millions of tracks ingested, analyzed, dissected to extract patterns. This practice provokes legitimate protests from artists who see their creations used without authorization or compensation to develop commercial tools.
In April 2024, more than 200 artists signed an open letter calling to "exercise vigilance not to endanger human creation."
The debate resembles the one agitating generative AI for images or text: where is the boundary between legitimate learning and unauthorized exploitation? Case law is gradually being built but remains largely uncertain.
Impact on Professional Musicians and Composers
It's hard to ignore the question: do these tools threaten jobs in music? The honest answer: yes, for certain market segments. Composers of production music or jingle creators could see their activity weakened.
On the other hand, artists who bring strong identity, recognizable signature, or the ability to create singular emotion won't be replaced by algorithms. AI excels in technical skill and reproducing existing styles, not in radical originality, creating new genres, or the emotional power that can intervene in music creation.
British musician Nick Cave expressed this distinction clearly in a newsletter to his fans:
"Songs arise out of suffering, by which I mean they are predicated upon the complex, internal human struggle of creation and, well, as far as I know, algorithms don't feel. Data doesn't suffer."*
This vision points to a reality: AI reproduces, it doesn't truly invent. Not yet.
The Question of Artistic Authenticity
Can we speak of art when a machine composes? This question runs through all technological revolutions in creation. We heard it with the appearance of photography ("it's no longer painting"), electronic music ("these aren't real instruments"), or autotune ("it's no longer real singing").
Each time, the debate eventually shifted: what matters isn't the tool but the intention, sensitivity, and choices of those using it. A photographer remains an artist even if the camera mechanically captures the image. An electronic music producer remains a creator even if they don't play any traditional instruments.
AI music follows this continuity. It expands the palette of possibilities, without resolving the fundamental question: what do you have to express and how do you do it?
Conclusion: AI as an Entry Point, Not as an End Goal
The accessibility of AI music creation represents an undeniable technological and cultural advance. It allows anyone with creative intent to materialize it without technical barriers. What remains is determining what you want to do with it.
For some, these tools will suffice: content creators seeking musical illustrations, entrepreneurs needing customized jingles, curious amateurs experimenting without investment. For others, they'll constitute a first step toward deeper music learning, a curiosity trigger that then leads to training, real instruments, human collaborations.
The mistake would be to consider AI as an endpoint. It remains a tool, remarkably powerful certainly, but one that doesn't think, doesn't feel, and will never create anything not already inscribed in its training data. Originality, artistic risk-taking, expressing a singular human experience: all this continues to fall under your responsibility.
So yes, create with AI. Explore its possibilities, test its limits, have fun with its sometimes absurd suggestions. But don't forget that the most sophisticated technology will never replace an essential question: what do you have to say, and why would music be the best way to express it?
FAQ
Can you create music with AI without any musical knowledge?
Yes, this is precisely the main interest of these tools. Consumer platforms like Suno, Udio, or Soundraw require no musical training. You describe what you want in natural language, and AI takes care of the rest. Obviously, some basic knowledge (recognizing a fast tempo from a slow one, distinguishing main musical genres, knowing some musical vocabulary) will improve your results, but they're not mandatory to start.
How much does AI music creation cost?
Most platforms offer limited free versions allowing you to generate a few tracks per day. For regular use, subscriptions generally range between $10 and $30 per month. More professional tools like AIVA reach $30-50/month for advanced features and extended commercial rights. If you're starting out, begin with free versions to test before investing.
Can AI-generated music be used commercially?
This depends entirely on the platform used and subscription type. Many free tools prohibit commercial use or require attribution credits. Paid plans generally include commercial rights, but read the terms carefully: some platforms retain co-ownership of creations, others limit the revenue you can generate, still others impose sector restrictions (no advertising, no streaming, etc.). Always verify before monetizing.
Is the quality comparable to human-produced music?
In certain formatted and relatively simple genres (mainstream pop, ambient electronic, lo-fi chill), results can fool an untrained ear. Quality has spectacularly improved since 2023. However, on complex compositions requiring subtle harmonic richness, elaborate arrangements, or emotional depth, the gap with professional human productions remains noticeable. For uses like content illustration, background music, or personal projects, quality is largely sufficient. For demanding commercial distribution, human touch-up often remains necessary.
Can you create any musical style with AI?
Theoretically yes, practically certain genres work better than others. Styles most represented in training data (pop, rock, electronic, hip-hop) give better results than niche genres (experimental free jazz, atmospheric black metal, contemporary classical music). Models also struggle with non-Western musical traditions less present in their learning bases. Expect more generic results on less common styles.
Must you declare that music was created by AI?
If you use music in a commercial context, the contractor may require it. It also depends on platform-specific conditions that may require being credited. Ethically, transparency seems preferable, especially if you claim an artistic approach. Some music distribution platforms (Spotify, Apple Music) are starting to request identification of AI-generated content, though rules remain unclear and poorly enforced. When in doubt, better to be transparent than risk later controversy.
How long does it take to create a track with AI?
Between a few minutes and several hours, depending on your requirements. Generating a first result literally takes 2-3 minutes on most platforms. But getting something that truly satisfies you generally requires several iterations, prompt adjustments, sometimes post-production touch-ups. Count on half an hour to an hour for a simple track you'll be happy with, more if you're aiming for a more polished result or combining multiple generations.