If you are a company that manages music assets on its own platform, the Cyanite Music Analysis API is the right choice for you. It allows you to utilize all of Cyanite’s capabilities directly in your interface, making it a part of your user experience. Our GraphQL API offers unparalleled flexibility for your platform – enhancing your music library organization tailored to your needs.

Here you can find the API’s full documentation: https://api-docs.cyanite.ai/

Cyanite API Benefits

Ease of Use

The API is a two-way highway letting you easily upload tracks directly from your system into Cyanite and get music AI music tagging or music similarity search results with unprecedented accuracy within seconds. All without your users even noticing that they are using Cyanite while it’s seamlessly embedded into your music library and their workflows on your platform. 

Each free Cyanite account can access the API on a small analysis contingency for testing purposes. To extend the testing scope, please reach out to business@cyanite.ai.

Speed of Deployment and Integration

We understand that your business is unique with individual requirements. That’s why we built a GraphQL API to read data from our service rather than traditional static REST APIs. GraphQL has the advantage of supporting any customization needs  – flexibly sending data – allowing you to change workflows as-you-go while you learn more about your users’ behavior with Cyanite.

Curious to learn more about the integration process? Just reach out to us via e-mail.

Quality of Support

We focus on making the API documentation as clear, up-to-date, and fun as possible. We explain the integration step-by-step and show examples while being your hands-on support in the integration. We have seen customers go from no AI to all Cyanite features in a matter of a few weeks.

API Scope

A basic rule of thumb is that everything you see on the Cyanite Web App is also possible on your own platform.

AI-Powered Music Tagging

To get an impression about the scope of Cyanite’s AI-powered music AI music tagging, we advise using our tagging taxonomy. Cyanite not only delivers a rich set of tags for a song as a whole but provides the same level of depth for every 15-second segment. This way you can map out dynamics and changes in energy, key, instruments, vocals, and other useful data points.

Music Similarity Search

Cyanite can deliver similar-sounding songs to any reference audio file or YouTube link. Upload a song as a file or insert a link. The music then gets ingested, analyzed, and compared to your music library. The reference song is stored in your library to enable flexible adjustment of the target segment in the song. You might want to try out similar results to the chorus versus the verse etc. for best results. With Cyanite’s Similarity Search, this is possible. To learn more about Cyanite’s Similarity Search, check out this article.

For Spotify, we are even able to use Spotify track IDs which results are stored for an even faster delivery of similar songs. Bear in mind that we are using a standard 30-second preview instead of the whole song for this.

Free Text Search

Simply ingest whole sentences and let Cyanite’s Free Text Search do its magic. Free Text Search understands the semantics of whole sentences, be it a complex musical description or the outline for a movie scene. Free Text Search eliminates guardrails in music search and opens it up to any audience to search and find tracks. To learn more about how to prompt our Free Text Search, check out this article.

Crates

Some of you might want to organize your music in more than just one library – or a music similarity search on just a part of your catalog. For those use cases, we offer Crates. With Crates, you can define subsections of your library to then perform music similarity searches. This way you make sure that specific users can only see certain parts of the catalog instead of everything.

How have others used our API? 

click on the pictures to get redirected to the websites.

Go ahead and start coding

Contact us with any questions about our music analysis API services via business@cyanite.ai. Don’t hold back from giving feedback on what we can improve.

Anyone can create an API integration. Just sign up with the button below.

If you are a coder and want to join the ride, please send your application to careers@cyanite.ai.

FAQs – API Integration

Artificial intelligence is an innovative technology. Pair it with a music library, and you get innovative results. That’s largely thanks to an AI music approach called MIR – Music Information Retrieval. What’s great about approaches like MIR is that they give you the power to find the exact song you’re looking for.

There are many ways you can integrate AI into your catalogue. We’ve identified three that are both easy on the eyes and rich in information. Let’s dive into the three most effective tools for presenting AI-generated results in your library or online platform.

Mood/Colour Visualisation

The world is a colourful place. You’ll find different shades and hues everywhere you look. And that’s great, because colours are intuitive to understand. We’re used to making sense of the world through them. Traffic lights and street signs work that way. So does fashion.

There’s also a clear connection between psychology and colours. Whether natural or learned behaviour, we attach moods to colours. If someone mentions sad, what’s the first colour you think of? What about happy, angry or excited? You probably guessed right, and you didn’t have to think about it for very long either.

Because music is an art form that’s all about mood and emotion, it makes sense to match songs to colours. That’s exactly what the University of California, Berkeley did. They surveyed 2500 people from the United States and China. The aim was to test their emotional responses to thousands of songs from genres such as classical, rock, jazz, folk, experimental and heavy metal. Researchers then determined 13 feelings to map out the subjective experience of music: “Amusement, joy, eroticism, beauty, relaxation, sadness, dreaminess, triumph, anxiety, scariness, annoyance, defiance, and feeling pumped up.”

Colour-based visualisation is great because it’s easy to navigate and provides analysis-driven results. Songs are grouped by emotion, and you see exactly how many fill each group. You also get a thorough first impression about the general structure of the song.

If you want to see (and hear) for yourself, check out this interactive audio map created from the data. Listeners can switch tunes to try out specific moods, and see how much of an emotion is present in a song. (50% romantic, 25% dreamy and 4% nauseating sounds like a rock-solid combination.)

Song Maps

While visual maps present a clear picture of your library, song maps connect the dots. Generally, we use maps for navigation; to plot paths from one point to another. This helps us see where things are located in relation to each other. Once you find where you are on a map, you can determine exactly where you’re going and what route to take.

Song maps imitate this process of discovery. A popular service like Gnoosic allows users to enter the name of an artist and discover those that are similar. Whatever you type in will bring up a tree of new artists to look at.

This makes browsing easy, because you already have a clear starting point. The more similar an artist the closer they’ll appear on the map. Type in Eminem and you’ll see Tupac right next to him. Michael Jackson, however, is right at the edge of the map. Interestingly, Eminem has said Tupac influenced his song-writing –  song structure is one of the components AI can look at when performing search.

As we’ve seen during the coronavirus quarantine, embracing novelty, whether in technology or content, is both healthy and progressive. AI-powered song maps are useful, intuitive tools to discover new music. If you’re up for adventure, you’ll try what’s on the edges. And if you want something closer to your favourite Jazz musician, you’ve got that too. It even groups the results into clusters. That means if something is a bit different, and you like that, you can find a group of artists that are similarly different.

Similarity Search

Similarity search takes a reference track and gives you a list of songs that match. (This works by pulling metadata and other relevant information from audio files). It does this quickly – we’re talking a matter of seconds.

It’s also more accurate than other methods, because the results are narrowed down to a small selection. Still too many matches? With Cyanite’s Similarity Search, you can filter the AI’s results based on the level of similarity.

This discovery-driven approach emphasises context; the search spans your entire catalogue. This could be helpful to catalogue owners who want to see how different songs within their database relate. You could check if there are more happy than sad songs, for example, and how best to update your library.

It also helps music publishers answer synch briefings faster. They can go from a reference track to the required music quickly, even if they’ve never used the database. You can try out Cyanite’s Similarity Search with a limited database to get a better feel for this application.

With a clear overview of the data, you can prepare your catalogue accordingly. A similarity search approach is functional, specific and visually simple. Users only discover what they’re looking for: the most similar tracks.

If you want better results – whether in your library, catalogue or user experience – delivered by innovative AI technology, give us a shout.

You can schedule a free 15-minute call with our CEO, Markus.

For more of our thoughts on Artificial Intelligence and Music Catalogues, check these out:
 
3 Reasons Music Catalogues Should Embrace AI Innovation During The Corona Quarantine

Free White Paper: 8 Tips How To Organize Your Music Catalog Better

4 Technology Trends Music Catalogue Owners Need To Know