Artificial Intelligence (AI), this is what the future of the music industry sounds like

For a few years now, music businesses have been using AI technology to automate the process of developing, mastering and categorizing audio and to create personalized experiences for listeners on streaming apps.

In the future, AI technology will be developing further to offer more options for the music industry to use.

In this blog post, we share our insights about the future of music with AI technology.

Four examples of AI technology in the music industry today

Many people think about robots or algorithms making music when they combine AI and music together, but what are the other applications of AI technology in the music industry that can currently be used by record labels and music distributors? We share four examples with you.

1. AI tagging and categorizing catalogs

In order to find certain songs and recommend the perfect music from a music catalog, the library must be well-categorized. But when you’re dealing with a big catalog, the chances of not getting songs picked up or sync because of differences in metadata, are quite high.

This can be due to multiple people working on tagging, having different interpretations or implementing external catalogs with different metadata. That’s why you need a clear structure based on the same methodology as the person curating the library and recommending music to users.

For most professionals working in music distribution companies and record labels, categorizing and tagging songs is not their favorite work.

White-label SaaS solutions like SonoSuite provide a unique Quality Control service that basically saves you time by checking and approving the content you upload -from metadata to audio quality- to DSPs to ensure you are compliant with each service guidelines.

Another option many use is auto-tagging, where you don’t have to manage tags manually when adding metadata to the songs you upload to streaming platforms. In such a case, AI does the tagging for you.

The German tech company Cyanite, for example, offers artificial intelligence (AI) to easily search, analyze and tag music.

The idea is to analyze music with neural networks. To do so, all that is needed is to collect data, preprocess audio data, and train, test and evaluate the neural network.

That way, the AI automatically learns to extract and classify the essential elements of songs and adds this new data to the songs as tags. As a result, music within a catalog can be easily categorized, optimizing search requests and synching.

While many music business professionals prefer to check tags manually, since AI technology still isn’t extended and doesn’t think like a human – while the curator mostly is- for many, AI tagging is a great alternative as it makes the process a lot easier and quicker.

2. Similarity search

Big record labels like Universal Music already use similarity search to find tracks within a catalog that sound similar.

When you have a song in mind and are looking for something similar, the AI similarity search feature listens to the song and automatically searches for similarities in the songs from the catalog that a record label or music distributor offers.

It works especially well in combination with AI tagging, because the algorithm knows what it should be looking for.

“What I love about automatic tagging and AI-driven similarity search is that it makes existing processes and workflows much faster without removing the human element, but rather strengthening it.” – Jakob Höflich, Co-Founder & CMO at Cyanite

3. Recommendations and personalized music

With so many songs that are already published and the new songs that are being brought to life every day, it can be an impossible task to find the perfect track for – let’s say – a certain moment, media production like a movie or event.

This is even harder when a record label or distributor has a large music catalog. There are always potential songs that never get on the radar of the curator.

Streaming services like Spotify, KKBox and the Asian company Tencent (channel integrated in SonoSuite) backed apps JOOX and QQ Music are already using algorithms to recommend music to users, offer personalized playlists, in-app notifications and create an optimized experience.

AI technology also helps to bring the right paid (sponsored) content to the right target audiences.

Playlists appearing in these streaming services are based on factors like location, which songs and artists the user plays the most and similar music. It’s even possible to choose a personalized playlist based on the user’s mood.

However, streaming app users aren’t the only ones who can enjoy and benefit from personalized music. Think about recommending soundtracks to video editors or specific pieces of audio to DJs who use them during a live set to get the vibe of the crowd.

4. Music mastering

Most streaming platforms require certain music criteria, like for example sound quality, to be fulfilled in order to proceed with distribution.

But music mastering always takes a lot of time, expertise and a good pair of ears. The use of AI technology for music mastering can help to meet the required criteria easily.

AI technology has been trained in mastering techniques and on what most music producers perceive as good quality.

The additional good news is that for most professional music mastering experts, AI technology for mastering is also being used for other target groups than their own. It’s also less complex than the work from a human mastering expert.

What does AI technology have in store for the future of the music industry?

Looking ahead, the music industry will probably fully embrace all the options named above. In the future, this technology will be even more enhanced to be integrated easily.

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These are only the four biggest examples and the most relevant to music distributors and record labels, but there are many other ways AI technology can be applied within the music industry. Think about AI generated music videos, for example.

Just think about how our smartphones, smartwatches and fitness trackers are already able to read our heart and breathing rates, stress levels, temperature and voice. It’s not unthinkable that AI could be used to recommend playlists and songs based on your mood or an activity you’re doing.

Music streaming apps can automatically provide the right music when the technology sends an alert saying that you’re working out, waking up, going to sleep or you’re stressed out and need some peaceful music to relax.

Music will be more data-driven or even generated by data (AI generated music), and the question is if it will feel the same for us to listen to it. Whatever happens with the music industry in the future, one thing is clear: it’s happening at a rapid pace.