AI in Music Creation: Top AI Music Generator Software for Musicians
A robotic system “playing” a keyboard – a creative metaphor for AI-driven music composition.
Artificial intelligence is rapidly transforming music production. Today’s AI music software can assist in composing original songs, generating ideas, and even automating production tasks. These tools leverage machine learning to analyze vast libraries of music and then generate new music in various styles. This opens up opportunities for musicians and producers of all skill levels – from beginners with no theory background to professionals seeking inspiration. In fact, AI music composers have become increasingly sophisticated, enabling even novices to bring their sonic visions to life with minimal effort (industrywired.com). Rather than replacing human creativity, AI in music creation serves as a collaboration partner – handling the heavy lifting of genre emulation or accompaniment, so you can focus on refining and personalizing the music.
In this article, we’ll explore six of the most relevant AI music generator software options available to musicians and producers. We’ll provide an overview of each tool’s features, pricing, and strengths, then compare their differences and ideal use cases. Whether you’re looking for an AI music generator online to crank out quick background tracks, or an advanced algorithm to inspire your next composition, these tools cover a wide range of needs. Let’s dive into the top AI music generators:
1. AIVA (Artificial Intelligence Virtual Artist)
A peek at AIVA’s interface, showing its library of preset composition profiles (genres like EDM, Lo-Fi, ambient, etc.) that can be used as starting points.
AIVA AI is one of the pioneering AI composition platforms, known for its focus on symphonic and soundtrack-style music. AIVA was originally trained on classical and film scores, enabling it to compose music with emotional depth suited for movies, games, or orchestral arrangements. Over time, it has expanded to over 250 profiles including jazz, pop, rock, electronic, and more. Musicians can choose a preset style (or even upload an influence track) and have AIVA generate an original composition in that vein. Uniquely, AIVA lets you download the result as an audio file and as MIDI, or even music notation, so you can further edit or orchestrate the piece yourself.
Features: AIVA’s strength is in generating structured, complex compositions that sound quite human. It often provides a rich harmonic structure – great for emotional soundtracks and cinematic themes. For power users, AIVA includes an advanced editor: you can tweak the generated score via a piano-roll interface, change instruments, adjust chord voicings, and more. There’s also an “Influences” feature – feed AIVA one of its own outputs or any MIDI as a style influence to shape subsequent creations. AIVA functions primarily as an AI MIDI generator, producing compositions that you can import into a DAW and assign your own instrument sounds if desired. This makes it popular among composers who want AI assistance in musical ideation while retaining control over the final sound design.
Pricing: AIVA offers a free tier (no credit card required) with limited usage, ideal for trying it out. The free plan allows 3 downloads per month of generated compositions (MP3 or MIDI) for non-commercial use (you must credit AIVA, and AIVA retains copyright). For expanded use, AIVA has paid subscriptions. The Standard plan (around €15 EUR/month if paid monthly) permits 15 downloads per month and allows monetization on certain platforms (YouTube, Twitch, etc.), though AIVA still owns the composition’s copyright. The top-tier Pro plan (~€49/month if monthly) offers 300 downloads, higher audio quality (WAV stems), and crucially gives you ownership of the copyright on the music you create. In other words, Pro users can use AIVA-generated music without attribution and with full commercial rights, which is important for professionals scoring films or games.
Strengths: AIVA excels at orchestral and soundtrack music – its pieces have complex structure and emotional dynamics that can sound quite convincing for dramatic contexts. It’s also versatile across genres (classical, jazz, pop, etc.), making it a powerful composition partner. The ability to output MIDI and sheet music is a huge plus for producers who want to customize arrangements with their own instrumentation. AIVA’s user base includes game developers, filmmakers, and songwriters who need an inspiring starting composition or backing track.
Drawbacks: Because of its pedigree, AIVA’s default output can lean toward the cinematic/classical side even when emulating modern genres – it prioritizes rich harmonies and may not always produce a catchy “hook” or simple pop song structure. While the advanced editing features are great, truly fine-tuning a composition in AIVA can be complex if you’re not familiar with music theory or MIDI editing. Also, the free plan’s limits mean you might quickly run out of downloads if you’re actively experimenting.
Ideal Use Cases: AIVA is best for composers and producers who want high-quality, structured compositions – for example, scoring a short film, adding an orchestral layer to a song, or prototyping song ideas. It’s an excellent tool if you need an AI MIDI generator to spark your creativity, which you can then build upon. If you’re a musician with some production skills, AIVA can save you hours by generating a chord progression and melody that you can then orchestrate and polish. However, if you just need quick loops or simple beats, other tools might be more straightforward.
2. Amper Music (by Shutterstock)
The Amper Music logo and tagline after its acquisition by Shutterstock. Amper pioneered quick, on-demand music generation for content creators.
Amper Music is a well-known name in AI music generation, notable for its user-friendly, minimalist approach. Founded as one of the first AI music companies, Amper’s platform allows users to create custom soundtracks in seconds by selecting a few basic parameters. It’s particularly tailored for content creators like podcasters, YouTubers, and marketers who need royalty-free background music. In 2020, Amper was acquired by Shutterstock, so it’s now part of Shutterstock’s offerings (often branded as “Amper by Shutterstock”).
Features: Amper’s interface is extremely simple. You start by choosing a genre, mood, and duration for the piece. Amper then generates a unique track matching those criteria. If you want to refine the result, you can adjust things like instrumentation intensity via dropdown menus (for example, make the drums more energetic, or swap a piano for a guitar) and re-generate the track. This no-theory-required editing means even non-musicians can orchestrate the music at a high level (e.g., dialing up or down the percussion, changing the mood from “bright” to “brooding”) without dealing with individual notes. Amper emphasizes fast soundtrack creation – it can churn out a new track in a matter of seconds after you set your parameters. The musical results tend to have solid production quality and coherence, suitable for background music in videos or podcasts. Amper also provides a global perpetual license for its generated music, so users don’t worry about copyright strikes on any platform. This was a big selling point: once you’ve paid for a track, you can use it anywhere without additional fees.
Pricing: Notably, Amper used to be free to compose with – you could generate and listen to as many tracks as you wanted. You only paid when you wanted to download a track for use. The cost was on a per-track basis: roughly $5 for a basic license and up to around $100 for a full commercial license (e.g., usage in a large-scale production). This à la carte pricing differed from subscription models and was attractive to those who just needed one or two tracks. Since the Shutterstock acquisition, Amper’s standalone website tools have at times been in flux; currently, Shutterstock offers Amper-generated music through its platform. This might involve a subscription to Shutterstock’s music library or on-demand purchase – in essence, the model of paying per download/usage persists.
Strengths: Ease of use is Amper’s hallmark. It’s arguably the most straightforward AI music generator software for someone with zero music experience to get a decent-sounding track. There’s no complex interface – just pick a style and hit create. It’s also quite fast. This makes Amper ideal for quick turnaround needs (say you’re editing a video and need a 30-second intro jingle ASAP). Another strength is the licensing clarity: once you pay for an Amper track, you have broad rights to use it, which is reassuring for commercial projects. Musically, Amper’s outputs are polished in production (good mixing and sounds) and work well as unobtrusive background music. It tends to nail genre-specific patterns (for instance, if you request an ambient track vs. a hip-hop beat, it will adhere to those genre conventions).
Drawbacks: Because Amper optimizes for speed and simplicity, the music it generates can sometimes sound a bit generic or repetitive. Users and reviewers have noted that Amper’s compositions focus more on chord progressions and atmosphere than on memorable melodies. In other words, it excels at creating a vibe but not necessarily a hummable tune. The customization, while accessible, is also relatively limited compared to something like AIVA – you’re not editing notes directly, just high-level attributes. Also, with the shift into Shutterstock, individual hobbyist users might find it less clear how to use Amper for free or at low cost (the old free preview model might require a Shutterstock account now).
Ideal Use Cases: Amper is perfect for content creators, podcasters, video editors, and small businesses in need of affordable music. If you’re producing a podcast and need intro/outro music, or if you’re a YouTuber who wants a consistent background track for your vlogs, Amper can deliver it quickly. It’s also useful for prototype scoring – for example, a game developer might use Amper to temp score a level with mood-appropriate music before investing in a custom score. For musicians, Amper might be less directly useful as a creative tool (since it doesn’t output MIDI or let you deeply tweak the composition), but it could still serve as a quick idea generator or backing track creator for practice and jamming.
3. Boomy
Boomy is a popular AI music generator online that aims to “democratize music creation”. Its promise is bold: with Boomy, anyone can create original songs in seconds – even if you’ve never made music before – and potentially earn money from them. Launched in 2019, Boomy has garnered attention for allowing users to generate songs and upload them to streaming services like Spotify, where the user can collect royalties if people listen. The platform has even reported that a significant number of the world’s “new” songs each day are being created on Boomy, highlighting how prolific its user base is.
Features: Boomy’s workflow is extremely simple and fun. After creating a free account, you click “Create a Song” and choose from a variety of genres/styles (such as Lo-Fi, EDM, Rap Beats, Global Groove, etc. – Boomy is always adding new styles). Once you pick a style, Boomy’s AI engine generates a complete track for you – complete with arrangement, instrumentation, and mix. This usually takes under a minute. You can listen to the result and use some basic editing tools if desired: for instance, you might adjust the tempo, change the mix by making certain instruments louder/softer, or regenerate a section for a slightly different vibe. The editing interface is more playful than a pro DAW – sliders and buttons rather than a full multitrack timeline – which lowers the learning curve. After tweaking, you save the song. Boomy then allows you to release the track to streaming platforms directly through its interface. With a few more clicks, your AI-generated song can be sent to Spotify, Apple Music, TikTok, and other platforms under your artist name. Boomy handles distribution and even provides cover art generation (they integrated an album art generator using DALL-E).
One distinctive Boomy feature is its monetization model. Boomy doesn’t charge users upfront or per track; instead, if you release songs through them, they take a 20% cut of any streaming royalties your tracks earn (the user gets 80%). This means you can use Boomy entirely free and even get paid, although you’ll need a lot of streams for significant earnings. Boomy also fosters a community – there’s a social feed where you can share songs and listen to what others have made, and they have periodic contests and featured playlists. Technically, Boomy’s outputs are royalty-free and belong to the user, so you can also download your tracks as MP3 and use them in videos or projects (Boomy grants you a license for any track you create). Advanced users can export to STEM files for remixing in a DAW if they have a paid plan.
Pricing: Boomy is free to start. You can create an account and immediately begin generating songs at no cost. The free tier does have some limitations – for example, as of recent updates, free users can save up to 5 songs to their account and release 1 track to streaming. Boomy offers a couple of paid plans (Creator and Pro) which increase these limits: the Creator plan (~$9.99/month) lets you save more songs and release more tracks per month, while the Pro plan (around $16.99/month) offers unlimited saves, more frequent releases, higher audio quality downloads, and other perks. Importantly, the core generation capability is the same; paid plans mostly lift quotas and add convenience. Many users stick to the free tier and simply delete or replace songs as needed to stay within the 5-song save limit, or they upgrade if they get serious about building a catalog. As mentioned, Boomy does not charge for distribution – instead they share streaming revenue (keeping 20%). If you don’t care about streaming, you can still use Boomy to make music for personal use without paying anything.
Strengths: Boomy’s biggest strength is accessibility and speed. It truly enables non-musicians to create a song at the click of a button. For content creators who need background music, Boomy is a treasure trove – you can generate multiple tracks until you find one that fits your video or podcast. For aspiring artists who don’t play instruments, Boomy offers a pathway to create original tracks and put them out in the world. The integration with streaming platforms is seamless, making Boomy a one-stop solution from creation to distribution. Another strength is the variety of styles: Boomy’s genres cover a wide spectrum (including holiday music, rap beats, and more experimental AI-generated soundscapes). The ease of monetization is also unique; some Boomy users have actually amassed thousands of streams online. From an educational perspective, Boomy can be a great way to learn about song structure – by observing what the AI creates, new musicians can pick up ideas about how intros, verses, and chorus sections might be arranged.
Drawbacks: On the flip side, music purists might argue that Boomy’s songs can sound formulaic or low-complexity. The AI uses loops and patterns that, to a trained ear, might feel repetitive or obviously machine-generated. Melodies are often simplistic. This is somewhat by design: Boomy optimizes for listenability and genre consistency rather than innovation. So while the tracks are perfectly serviceable, they may lack a certain human touch or uniqueness that comes from deliberate songwriting. Another consideration is that because Boomy is so easy, there is a flood of AI-generated music on streaming platforms – making it hard for any one track to stand out. Also, if you want to do more detailed editing (like change a chord progression or edit MIDI notes), Boomy doesn’t offer that – you’d have to export the stems to a DAW, at which point you might consider using a different tool from the start. Finally, reliance on Boomy’s distribution means your music is tagged as created via Boomy (for transparency on streaming services), which some artists might not prefer.
Ideal Use Cases: Boomy is ideal for hobbyists, content creators, and artists-in-training. If you want to quickly generate music for a background soundtrack (e.g., Twitch streaming background music, YouTube vlog music, indie game developers needing some quick tunes), Boomy is a fantastic choice. It’s also a fun creativity toy – many users just enjoy making songs for personal enjoyment or to share with friends. For a singer or rapper who doesn’t produce beats, Boomy can provide instrumental tracks to write vocals over. Additionally, educators have used Boomy to teach kids about music by letting them experiment with creating songs. If your goal is to create polished, chart-ready songs, Boomy might not get you all the way there, but it could be a starting point for ideas or a quick way to generate a demo. Overall, Boomy shines in making music creation accessible and engaging, removing virtually all barriers to entry (techradar.com).
4. Soundful
Soundful’s homepage proudly advertises itself as “the world’s most advanced AI music generator,” highlighting its focus on high-quality, royalty-free music creation.
Soundful is a newer entrant in the AI music scene, positioning itself as a professional-grade AI music studio. Its platform empowers creators to generate royalty-free tracks at the click of a button, similar to others, but Soundful emphasizes quality and usability for serious musicians and content creators. In fact, according to Soundful’s team, a large portion of its users are music producers and singer-songwriters (not just casual creators), which suggests the tool is designed to meet higher musical standards. Soundful is often touted for its speed – generating a track in mere seconds – and the fidelity of its output, which can be surprisingly rich.
Features: Soundful’s workflow will feel familiar if you’ve used Boomy or AIVA. You start by selecting a genre or a “template”. Soundful offers a broad range of genres (hip-hop, EDM, Latin, pop, R&B, lo-fi, etc.), and within each, you might pick a more specific vibe. For example, under Latin you might choose “Latin Pop” vs. “Reggaeton” as different sub-styles. Once selected, Soundful instantly generates an audio preview of a track in that style – often within 5 seconds or so, which is impressively fast. You can then customize the track using a set of colorful controls. These controls allow you to adjust parameters like tempo (BPM), the key of the song (and toggle major/minor scale), and even the structure (you can regenerate specific sections like intro, verse, chorus to get a different musical idea for that part). You can also select from some pre-defined energy levels or “moods” to influence the track’s intensity. Soundful’s interface also includes a search bar where you can type the name of a popular artist or song; the AI will then try to create something inspired by that reference. For example, typing “Adele” might influence the generator to produce a soulful, piano-driven pop ballad (#Adele tag). This feature is reminiscent of searching loops on Splice – it helps users anchor the AI output to a familiar style or “vibe.”
When you’re happy with the track, you can download it. Soundful provides downloads in high-quality audio and even stems or MIDI for certain tracks, which is valuable for producers who want to fine-tune the mix or melody. All music generated is royalty-free for the user, though note that Soundful’s free and standard plans have some limitations on commercial use (full licensing for business use is available under enterprise plans).
Pricing: Soundful operates on a freemium model. There is a Free Forever plan that lets you generate unlimited tracks and download up to 10 tracks per month. Those downloads are for personal, non-commercial projects (like your YouTube channel, school project, etc.). The free plan is great to test the waters and even use in some content, but if you’re a prolific creator, you might hit the 10-download cap. The Premium plan comes in at $7.42 per month (billed annually, around $89/year). Premium allows unlimited downloads and usage of tracks in your content, but notably does not automatically grant full copyright ownership – Soundful retains some rights or requires an enterprise license for certain commercial usages. Essentially, if you’re a business or need exclusive rights (like for a big film or to relicense the music), you’d go to a Custom Enterprise plan to negotiate that. For most indie creators, the Premium plan covers things like YouTube monetization, podcasts, and even indie game music, with the understanding that the music was AI-generated on Soundful. Always double-check the latest license terms, but Soundful is transparent that the low-cost premium is for broad but not all-encompassing usage.
Strengths: Soundful’s speed and quality combination is a major plus. In testing, users often comment how quickly it produces something that sounds “studio-ready.” The audio quality (samples, instruments, mix) is high, which is why many producers find it useful – it doesn’t immediately scream “computer made this.” The interface strikes a good balance between simplicity and control; it’s easy enough for non-musicians to use, but the options to change key, tempo, structure, etc., give experienced musicians the flexibility to shape the output. The artist/song reference feature is another strength, catering to those who want to emulate a certain style. Soundful also allows MIDI and stem downloads (on paid plans), which means you can take the building blocks of the AI composition and expand on them creatively in your own production environment – a big advantage for music producers who want to treat the AI output as a starting point or inspiration. Additionally, Soundful has a global playlist feature, where you can see tracks created by others and even use them as inspiration or starting seeds for new tracks (sort of a collaborative angle).
Drawbacks: One limitation is that Soundful’s editing capabilities, while solid, are not as deep as manually composing – e.g., you can’t specify a exact melody or upload your own tune to transform. It’s more about guiding the AI than directly authoring the notes. Compared to AIVA, it lacks a full composition editor; compared to Boomy, it has more options but maybe a slightly higher learning curve because of them. Another consideration is the licensing complexity: the fact that the affordable Premium plan omits transfer of copyright might be confusing. Essentially, if you just need music for your content, it’s fine; but if you intend to, say, release an album of AI-generated tracks under your name commercially, you’d need to clarify rights with Soundful (likely via enterprise terms). Some extremely discerning musicians might feel the outputs, though high quality, still have a “stock music” feel – meaning they are polished but a bit generic (a common trade-off in AI music). However, this largely depends on how you use the tool – starting from Soundful’s track and then adding your own vocals or instrumentation can yield very unique results.
Ideal Use Cases: Soundful is a great choice for content creators, streamers, and music producers who want quick results but aren’t willing to sacrifice quality. For a YouTuber needing a catchy background track that matches the energy of their video, Soundful can deliver it in seconds. For a game developer or app developer needing custom music loops, Soundful’s fast previews and high-quality outputs are valuable. Musicians can use Soundful to generate ideas for beats, chord progressions, or even full instrumentals to topline (write lyrics and melody over). It’s also useful for agencies or marketers who need “brand music” – for example, a short jingle or theme music for a product video – since you can iterate quickly through ideas and get a polished track without hiring a composer. Essentially, if you want royalty-free music on demand and care about having a bit of creative control in shaping it, Soundful is an excellent tool to try. It feels like having a super-fast session musician/producer at your fingertips.
5. OpenAI’s MuseNet and Jukebox (Research Projects)
No discussion of AI music generation would be complete without mentioning OpenAI’s music generators, which, while more research-oriented, have significantly influenced this field. OpenAI is the organization behind GPT-3 and ChatGPT, and they’ve applied similar AI breakthroughs to music as well. Two notable projects are MuseNet and Jukebox. These aren’t commercial software like the others in this list, but they are freely available research models that tech-savvy musicians can experiment with – and they illustrate the cutting edge of AI in music.
MuseNet: Introduced in 2019, MuseNet is a deep neural network that can generate 4-minute musical compositions with up to 10 different instruments (openai.com). What made MuseNet famous is its ability to blend styles; for example, you could prompt it to compose a piece “in the style of Mozart, but as a jazz song” or “a country song with Beethoven’s harmony”. MuseNet was trained on a large dataset of MIDI files encompassing many genres and artists. It works by predicting the next note in a sequence (much like GPT predicts the next word in a sentence), allowing it to generate music that continues in a given style. During a period in 2019, OpenAI even provided a web demo where users could choose a style and MuseNet would generate music live in their browser – users could pick things like genres (classical, jazz, pop) and specific composer or band styles (Chopin, The Beatles, etc.), and MuseNet would oblige by composing something that often surprisingly resembled the prompt. For instance, MuseNet could take a few opening bars of a Chopin piece and then continue it as if it were a pop song with drums and guitars – a fascinating blend that showed the model had learned stylistic patterns. MuseNet’s output is MIDI-based (it generates compositions, not rendered audio), so using it typically means feeding the output into a software instrument to hear the music. While the official MuseNet demo has since been taken down, the model itself was made available to the community (OpenAI shared some of the trained model files) and an open-source community version exists. MuseNet demonstrated that AI can capture high-level musical structure over minutes of music, a big leap at the time.
Jukebox: In 2020, OpenAI took AI music a step further with Jukebox
. Jukebox is a neural network that directly generates raw audio, complete with singing and lyrics (unlike MuseNet’s MIDI approach). It was trained on a huge dataset of music of various genres and even specific artists. The goal was to have the AI produce new songs in the style of say, Elvis Presley or Beyoncé, complete with computer-generated vocals that sound like the artist (albeit with a somewhat surreal quality). Given a genre, an artist style, and even snippet of lyrics, Jukebox will try to create an original song. For example, you can prompt it with “Frank Sinatra crooning heavy metal” or provide custom lyrics for it to sing. The results from Jukebox were astonishing and bizarre in equal measure – you’d hear what indeed sounds like an unreleased track by a famous artist, but the lyrics might be gibberish and the coherence can drift. The audio quality is also a bit raw/fuzzy since generating high-fidelity audio is extremely challenging (Jukebox operates at a lower sampling rate to manage complexity). OpenAI open-sourced Jukebox’s code and model weights, allowing developers and musicians to play with it. Running Jukebox requires heavy computing power (it’s slow without a top-tier GPU), so it’s not a plug-and-play app for most people. However, it’s a landmark in AI music because it showed AI could handle the full stack: composition, arrangement, and performance (singing). Jukebox outputs actual audio (usually around 1-2 minutes long samples) that you can listen to as complete songs by an AI.
Using OpenAI’s tools: Unlike AIVA, Amper, Boomy, and Soundful, which are user-friendly services, MuseNet and Jukebox are more like playgrounds for AI researchers and adventurous musicians. MuseNet (if you get the model) or its community implementations let you generate MIDI by writing some code or using a command-line interface. Jukebox similarly requires using code or Colab notebooks provided by the community to input your prompts and receive an audio file output. There is no polished GUI by OpenAI for these now (though third parties have built interfaces in some cases). Another OpenAI model worth mentioning is MusicGPT / MusicLM (in development), but as of this writing, OpenAI hasn’t released a new public music tool beyond MuseNet and Jukebox.
Strengths: The key strength of OpenAI’s models is innovative capability. They aren’t constrained to royalty-free stock sounds or preset genres – they literally learned from heaps of real music, so they can generate things that are quite complex and novel. MuseNet’s strength is long-form structure and multi-instrument compositions (it can handle switching styles mid-piece or doing mashups that would be hard for other AI). Jukebox’s strength is that it creates actual performances – the fact that it can mimic an artist’s vocal style and timbre is incredible. These tools are open-ended; for a creative musician with coding skills, they offer a treasure trove of experimentation. They are also open source AI music generators in spirit – Jukebox’s code is public, allowing the community to tweak and learn from it.
Drawbacks: For the average musician, these are not turnkey solutions. The technical barrier is high if you want to use them deeply. Also, the outputs often need cleanup – e.g., MuseNet’s MIDI might be musically inconsistent in places that you’d have to edit, and Jukebox’s audio, while intriguing, often isn’t polished enough to use directly in a song release (and it’s hard to alter after the fact, since it’s baked audio). Another issue is that because these models learned from existing music, there are ethical and legal considerations (for example, if Jukebox produces something that sounds too much like The Beatles, including their vocal likeness, it raises copyright and cloning questions). OpenAI itself positioned these as research, not commercial music production tools.
Ideal Use Cases: If you’re a music producer who is also a bit of a coder or very tech-savvy, you might use MuseNet or Jukebox to generate inspiration that you then import into your workflow. For instance, a producer might generate a few Jukebox samples in the style of a favorite artist and then sample or chop them up into a new piece (bearing in mind copyright issues). Or a composer could use MuseNet to get an AI’s take on how to continue a melody. These tools are also ideal for experimental artists who want to push boundaries – say, creating generative music art projects. For most mainstream musicians, OpenAI’s models are more of a curiosity or preview of what future AI music might offer. In summary, MuseNet and Jukebox are milestones that show the potential of AI music generation – merging styles, handling raw audio, and inspiring the next generation of AI music software – even if they’re not daily-driver tools for producers just yet.
6. Google’s Magenta Project and MusicLM
Google has been actively researching AI for music as well, primarily through the Magenta project and more recently with a system called MusicLM. While not a traditional “product” like Boomy or Soundful, Google’s AI music efforts are influential and offer tools that musicians can explore, especially those interested in open-source and cutting-edge tech.
Magenta (TensorFlow Music Projects): Magenta is an open-source research project under Google Research that has produced a variety of AI music generation tools. It’s essentially a library (built on TensorFlow) plus a collection of pre-trained models for tasks like generating melodies, drum beats, or even sketching entire songs. Over the years, Magenta released some interactive demos and plugins; for example, AI Duet (where you play a melody on a piano and the AI responds to it) and Magenta Studio (a set of plugins for Ableton Live that could do things like continue a melody, generate drum patterns, or add harmonies to a MIDI track). These tools function as AI MIDI generators – they produce note sequences that you then assign instrument sounds to. One Magenta model, MusicVAE, can interpolate between two melodies to create a new one (useful for transforming musical ideas gradually). Another, Melody RNN, generates monophonic melodies in a certain style. Because Magenta is open-source, many musicians with coding ability have tinkered with it to create custom AI assistants – for instance, an AI that generates jazz solos, or one that creates lo-fi hip hop loops. There’s even an open-source model called MidiMe that lets you train a custom AI on your own MIDI compositions and then generate similar music. For non-coders, the Magenta team’s Magenta Studio (Ableton plugins) was the most accessible, offering a simple interface to harness some of these models within a popular DAW. If you’re into the technical side, Magenta’s GitHub and community are full of resources to get started with training or using these models.
MusicLM: In early 2023, Google introduced an experimental AI model called MusicLM that took the AI music world by storm. MusicLM is a text-to-music generator – you input a natural language description, and it generates an original musical track that matches that description (blog.google). For example, you could type “a calming violin melody backed by a distorted guitar riff” or “80s synthwave with a strong bassline and female vocal humming” and MusicLM will attempt to create it. The results, showcased in Google’s research paper and blog demos, were remarkably coherent and high-fidelity compared to earlier attempts in this space. Unlike OpenAI’s Jukebox (which also could take some conditioning like lyrics or artist name), MusicLM is explicitly built to follow a descriptive prompt – making it more practical for users who don’t know how to write music but know what they want it to sound like. In the examples, MusicLM generated audio with appropriate instrumentation for the given mood and often with evolution over time (e.g., an ambient intro that builds into a beat, if requested). Initially, Google did not release the model to the public due to concerns about biases and copyrighted training data. However, later in 2023, Google made MusicLM available for the public to try via their AI Test Kitchen app (an experimental hub). Users can sign up and get access to a demo where you type prompts and get two variations of music to listen to, then optionally give feedback on which was better. This controlled release is letting Google gather feedback while people get a taste of prompt-based music creation. It’s still early, but MusicLM or its successors could become a powerful tool for musicians (“Describe your song idea and have the AI draft it for you”).
Other Google AI Music Efforts: Google’s research in music AI also spans AudioLM (a model for generating audio that is coherent over long terms, used in part for MusicLM) and some quirky tools like NSynth (Neural Synthesizer, which blended instrument sounds using AI) and Tone Transfer (which could transform a recording of you humming into an instrument like a flute using AI timbre transfer). Google also hosts the Doodle Audio experiments, such as the FreddieMeter (to see how close your singing is to Freddie Mercury) and more relevantly, Blob Opera – a fun AI-backed experiment where cartoon blobs help you create opera singing harmonies. While these are more for fun or specific tasks, they show Google’s broad interest in AI-generated and AI-augmented music.
Strengths: Google’s Magenta and MusicLM represent the state-of-the-art in many ways. Magenta’s strength is that it’s open and modular – if you have a very specific need (say an AI bassline generator), you can find or train a model for it with Magenta. It’s also a great learning resource for musicians interested in machine learning. MusicLM’s obvious strength is its intuitive interface (what could be easier than describing what you want?) and the quality of the output, which is some of the best heard from AI to date for multi-instrument, non-loop-based music. For those who got access, MusicLM is probably the best AI music generator online for sheer wow-factor – hearing your sentence turn into a never-heard-before piece of music is inspiring.
Drawbacks: The main drawback is accessibility. Magenta tools, beyond the few user-friendly offshoots, generally require coding knowledge. There’s also the creativity curve – using those tools well might require understanding their quirks and how to prompt or seed them effectively. MusicLM, meanwhile, isn’t widely available to everyone yet (and it’s not open source at this time), so one can’t rely on it as a production tool. Also, prompt-generated music might not easily align to a specific length or structure you need, and editing the output is non-trivial (you get an audio file, which you can chop but not change individual notes of, unless you also utilize some audio-to-MIDI transcription after the fact). For Magenta’s pre-trained models, some are focused (like a melody generator) so they don’t give you a full song – you’d have to integrate them into your workflow manually.
Ideal Use Cases: If you’re a developer-musician or an AI enthusiast, Magenta is a playground for creating your own smart music tools – for example, maybe you incorporate a Magenta RNN into a live performance to improvise along with you. For regular musicians, Magenta’s best use might be via the Magenta Studio plugins in Ableton Live, to assist with things like generating variations of a riff or filling in drum patterns. That’s a fairly practical way to let AI help in composition if you use Ableton. As for MusicLM, it points toward a future where any musician could use text prompts to get rough drafts of songs. Currently, you might use it for inspiration if you have access: e.g., type in creative prompts until you hear a melody or groove you like, then re-create or sample it in your own work (again, being mindful of the legal grey area). Also, music producers in media might use such a tool to quickly generate placeholder tracks for video edits (“I need 15 seconds of sad piano music here”) before later replacing it with a human composition. Overall, Google’s efforts are shaping the future but also providing some open-source AI music generators now (through Magenta) that you can tinker with if you’re inclined.
7. Cyanite (AI Music Tagging & Analysis)
Not all AI tools for music focus on generating new notes; some, like Cyanite AI, help with the analysis and organization of music. While Cyanite isn’t an “AI music generator” in the composition sense, it’s highly relevant to musicians and producers working with lots of audio. Cyanite is an AI-powered platform for music tagging, similarity search, and sonic analysis. Essentially, it listens to tracks and automatically figures out things like the genre, mood, key, tempo, energy level, and even writes a short description of the music – tasks that typically require human music experts. For anyone managing a music catalog (be it a music library, label, or a producer with hundreds of tracks), Cyanite can save a ton of time by indexing and making sense of your collection.
Features: Upload a song (or use their API for bulk processing), and Cyanite will generate a rich set of metadata tags for it. These include standard tags like genre (e.g., Indie Soul, Electro Pop) and mood (e.g., Feel-Good, Energetic), as well as tempo, key, vocal presence, and more (cyanite.ai). It even attempts to capture the “energy dynamics” over time and can tell if a track has vocals and what gender lead voice, etc. One standout feature is Auto-Description, where Cyanite writes a short natural-language blurb about the song’s vibe – for example: “Bouncy and bright, featuring breezy electric guitar and female vocals that create a feel-good mood.”. This kind of description can be immensely helpful for music supervisors or anyone pitching music for sync (film/TV placements), as it gives a quick insight into the track. Cyanite also offers a Similarity Search – you can input a reference track, and the AI will find other songs (from your catalog or their database) that are similar in sound and mood. This is useful for A&R (find me songs like this hit single) or for creators looking to license music (find me something that has the same vibe as song X because I can’t afford song X’s license). Additionally, there’s a free text search: you can type something like “uplifting acoustic folk with male vocals” and it will return tracks that match that description, which flips the auto-description around – essentially enabling a Google-like search for music in your library. For visualization nerds, Cyanite can also plot songs on graphs (for example, mapping energy vs. valence) to compare tracks, and provide “catalog insights” about trends in your collection.
Pricing: Cyanite has a free trial on their website where you can manually upload a limited number of tracks to see the tagging in action. For ongoing use, they offer subscription plans and an API for enterprises. The pricing isn’t publicly listed in detail (likely custom quotes for big clients like labels or libraries, and tiered plans for smaller users). For individual musicians, Cyanite might offer a small plan if you just want to analyze your own portfolio of songs. The value proposition is strong for industry folks: consider a music library with 10,000 tracks – manually tagging all that is impractical, but Cyanite can do it quickly. For an independent producer, using Cyanite on your album might be overkill unless you’re specifically needing those insights to market your music. However, as a free tool, it’s fun and useful to try on a few tracks – you might learn something about how an AI perceives your music’s mood!
Strengths: The obvious strength is speed and consistency. Cyanite can analyze large volumes of music in minutes, something that would take musicologists weeks to do by hand. And it applies a consistent standard, which is often hard to achieve with crowdsourced or human tagging (one person’s “mid-tempo” might be another’s “slow”). For producers, using Cyanite can give an objective second opinion on your track – e.g., confirming the BPM/key, or seeing what moods the AI thinks your song conveys. The similarity search is great for generating playlists or finding the right track for the right scene quickly. Additionally, for content creators who use stock music, a tool like this could help navigate libraries by mood rather than just genre keywords. Cyanite basically acts like an expert music librarian that instantly categorizes any track.
Drawbacks: Being an analysis tool, Cyanite doesn’t directly make music, so its “creative” use is indirect. If your goal is to compose, Cyanite doesn’t help with that. However, one could use it creatively by analyzing famous songs to see their mood/structure, or to find reference tracks during production. Another limitation is that AI tagging, while advanced, might not capture everything a human ear could – for example, lyrical themes might be beyond its scope (unless the lyrics are provided and it has NLP to analyze them). Also, if a track is very genre-blending or novel, the tags might not fully do it justice (AI can sometimes mis-tag if the music doesn’t fit its trained categories well). There’s also the consideration of cost – casual users might not want to pay for this, and free usage is limited. Lastly, like any AI, it can have biases based on its training data – it might categorize music from underrepresented genres less accurately if it was trained mostly on Western commercial music (just as a hypothetical caveat).
Ideal Use Cases: For music producers and artists, Cyanite can be a helpful tool in the post-creation phase. Suppose you finished an album – you could use Cyanite to generate descriptions and moods for each track, which can then be used in your marketing blurbs or when pitching to playlists (“This track has an energetic, confident electro-pop vibe,” etc., as confirmed by AI). If you’re uploading music to a stock library or sync agency, having consistent tags and descriptions via Cyanite could increase the chances of your music being found. For music supervisors, DJs, or playlist curators, Cyanite is a dream – it helps sort through large catalogs to find the right song for the moment. Even a casual user with a large MP3 collection could use it to organize their library by mood. In the context of AI music companies, Cyanite represents the analytical side of AI in music – complementing the generative tools. A scenario: a content creator could use Soundful to generate a bunch of tracks, then use Cyanite to tag them and pick the one that best fits the “mood” they need. In sum, while Cyanite won’t write your next song, it’s a highly relevant AI tool that can help get your music in front of the right ears by describing and categorizing it in smart ways.
Comparison of AI Music Tools and Choosing the Right One
Each of the AI tools above has its own niche, and the best choice ultimately depends on your needs as a musician or producer. Here’s a quick comparison and highlight of ideal use cases:
AIVA: Best for composers and filmmakers. It excels at complex compositions (especially orchestral, classical, cinematic pieces) and offers detailed control via MIDI. If you want an AI to help write a score or harmonic structure that you can then edit, AIVA is unmatched. However, it requires some musical understanding to fully leverage, and its pricing is geared toward those who need commercial rights. Choose AIVA if you’re scoring a film/game, need sheet music output, or want to experiment with AI-driven songwriting in a DAW environment.
Amper Music: Ideal for content creators needing quick, customizable background tracks. It’s like having a quick music library that generates on-demand. You won’t get a highly unique melody out of it, but you will get a solid, mood-appropriate track in seconds. If you’re a podcaster, YouTuber, or corporate video editor with no time to compose, Amper is a great choice. Its pay-per-download model is good if you only occasionally need tracks (you’re not stuck in a subscription when you don’t need music that month). On the downside, if you need many tracks regularly, other subscription-based tools might be more economical.
Boomy: The go-to for casual music makers and influencers. Boomy stands out for letting absolutely anyone create and publish songs. It’s perfect if you want to dabble in music creation for fun, release a novelty album, or generate lots of simple tracks for social media content. Boomy’s integration with streaming platforms also makes it the best choice if your goal is to become a Spotify artist quickly or generate passive income from streams (keeping expectations reasonable – it takes work to get significant plays). For more seasoned musicians, Boomy might feel limiting, but it can still be a quick source of backing tracks or ideas. It’s also kid-friendly and education-friendly due to its ease of use.
Soundful: Tailored for serious creators and producers who want speed but also a degree of quality and control. If you’re choosing between Boomy and Soundful for content creation and you have a bit more musical inclination, Soundful might win out because of its higher fidelity output and more adjustable parameters. It’s great for those producing professional content (YouTube, podcasts, games) who need music that can stand up in a mix without sounding cheap or canned. It’s also useful for musicians who need a spark – for instance, a beat maker can use Soundful to generate a base track, then layer their own melody or vocals on top. Budget-wise, Soundful’s premium is quite affordable, but remember to consider licensing needs. If you’re a brand or agency needing lots of tracks with full rights, you might end up on a custom plan.
OpenAI MuseNet/Jukebox: Best described as experimental tools – great for inspiration and innovation, not so much for day-to-day production (for most users). If you’re tech-savvy, these can be gold mines for creativity. For example, a film composer might use MuseNet to generate some thematic material in a different style to overcome writer’s block (“how would the AI continue this motif?”). Jukebox could be used to generate a fake “sample” in the style of an old artist which you then chop up in a hip-hop track. However, these require patience and technical know-how. They are ideal for researchers, experimental artists, or those who want to peek into the future of AI music. Not recommended if you need guaranteed results on a deadline or if you’re not comfortable with coding/Colab notebooks.
Google Magenta/MusicLM: Also in the realm of cutting-edge research, with Magenta providing open source frameworks for creative coders. If you’re a developer or into DIY music tech, Magenta is fantastic – you could integrate its models to build your own custom AI music assistant (some have even built AI plugins from Magenta models). MusicLM, once widely accessible, could become a game-changer for musicians who prefer describing things in words or for those who don’t play an instrument but have a song idea in mind. As of now (early 2025), MusicLM is more of a preview – something to keep an eye on as it develops. It highlights where things are headed: possibly soon you’ll have an AI music generator software where you just type “make me a MIDI guitar riff in blues style” and it happens. For now, consider Google’s tools if you lean toward the innovator/early-adopter side and are willing to experiment, or if you specifically use Ableton Live (try Magenta Studio plugins for fun).
Cyanite: A different beast – it’s not about creating music but about managing and enhancing music data. If you have a library of tracks (yours or a catalog you manage) and need to organize or make them findable by mood/genre, Cyanite is incredibly useful. For an independent artist, Cyanite can help craft better descriptions for pitching your songs, or simply help you understand how your track might be categorized by others. It’s the kind of tool you’d use in conjunction with one of the generators: e.g., after making 10 tracks with AIVA or Soundful, use Cyanite to tag them and pick the best ones for “uplifting” vs “melancholic” playlists. Cyanite is most directly valuable to music supervisors, library owners, sync licensing folks, and DJs creating sets by energy. But as AI becomes more common, even streaming services and social platforms might use similar AI tagging under the hood – so Cyanite gives a glimpse into that world (and you can leverage it to your advantage as a creator by speaking the same “language” of tags and moods).
In summary, there is no one-size-fits-all AI music generator – but that’s a good thing. Musicians and producers now have a toolkit of AI services to choose from. The casual creator might gravitate to Boomy for its sheer simplicity, the pro composer might favor AIVA for its depth, the content marketer might use Amper or Soundful for fast turnaround, and the experimentalist might play with OpenAI and Magenta. You might even end up using multiple: for instance, using Soundful to generate a beat, then using AIVA to add a string arrangement on top of it, and finally using Cyanite to tag the finished track for your portfolio.
One thing to keep in mind is that AI music generators work best as collaborators, not replacements. The magic often happens when you, the human, curate and post-edit what the AI gives you. Think of AI as your studio assistant: it can churn out 100 ideas, but you decide which one becomes a hit. Also, ethical and legal considerations around AI-generated music are still evolving (for example, copyright law is catching up to whether the AI or user owns the output, and how using an AI trained on existing music might implicate those original works). As a musician or producer, staying informed about these topics will be important as the technology becomes more mainstream.
Finally, the landscape of AI music tools is growing rapidly – new startups and even open source AI music generator projects pop up frequently. Some other notable mentions include Loudly (another online generator from a music library company), Beatoven.ai (focused on generating customizable music for videos/podcasts), Mubert (AI-generated music streams and an API for developers), and research projects like Meta’s MusicGen and Riffusion (which generates audio by visualizing sound as images). The ones we detailed, however, are among the most established or uniquely capable as of now.
Engaging with these AI tools can greatly enhance your music creation process. Whether you need a quick beat, a symphonic score, or just some fresh ideas, there’s an AI out there ready to help. Embrace them as new instruments in your arsenal. Much like synthesizers and drum machines were once new and somewhat controversial tools that became staples of modern music, AI generators may well become a common part of the music-making process. As a musician or producer, experimenting with AI may spark creativity in ways you didn’t expect – perhaps leading you to genres or motifs you wouldn’t have explored on your own. So don’t hesitate to try these platforms and see what you can create in collaboration with a little artificial intelligence. The future of music is here, and it sounds exciting.
Resources
IndustryWired – Top Free AI Music Generators of 2024 (Chaithanya, Apr 2024) – Overview of several AI music tools including Amper, AIVA, Jukebox, Soundraw, Boomy, Loudly, Beatoven.ai
OpenAI Blog – MuseNet (Apr 2019) – OpenAI’s announcement of MuseNet, describing its ability to generate 4-minute compositions with 10 instruments in various styles
OpenAI Blog – Jukebox (Apr 2020) – OpenAI’s report on Jukebox, a model generating music as raw audio (including vocals) and releasing its code and model weights for public use
Google AI Blog – Turn ideas into music with MusicLM (May 2023) – Google’s introduction of MusicLM, a text-driven music generator, and details on how users can try it via AI Test Kitchen
TechRadar – What is Boomy? Everything we know about the AI music maker (Max Slater-Robins, Feb 2025) – An in-depth look at Boomy’s features, use cases, and how it enables users to create songs and monetize them
Samantha Brandon – 6 Best AI Music Generators: March to Your Own Beat (Jan 2023) – A review blog comparing AIVA, Amper, Soundraw, Melobytes, Boomy, and Soundful, with hands-on impressions and pricing details.
SoundTech Insider – AIVA Review: Everything You Need to Know (2023) – Detailed breakdown of AIVA’s capabilities and pricing tiers (Free, Standard, Pro), including download limits and copyright details for each.
Attack Magazine – Will Soundful be a Friend or Foe for Producers? (Oct 2022) – Long-form article examining Soundful’s AI platform in the context of music production and the creator economy.
Cyanite.ai (Official Site) – Information on Cyanite’s AI music tagging and search products, including auto-tagging of genre/mood, auto-description generation
cyanite.ai, and similarity search functionality.
ToolsForHumans – Boomy AI Review 2025 – An overview of Boomy’s features and pricing (free vs. Creator vs. Pro plans), and discussion of user feedback regarding its strengths and weaknesses in music creation.