3 Reasons to Use AI in Music Industry
Artificial intelligence (AI) technologies are penetrating various business domains from retail to space engineering. But human sensory experience projected into the art is still the area where no algorithm can compete with artists. However, AI comes in handy for music composing, creating streaming platforms, and monetization. Read on to find out the nuts and bolts of recent AI-powered music applications and what to expect next in the industry.
AI for music composing
Per the McKinsey report, 70% of companies will adopt at least one of the AI technologies by 2030. The music industry is no exception. With the rise of TikTok and the growing sector of YouTubers that need music for their creativity, AI music-generative tools enter the picture.
Take Amper, a tool powered by AI for creating original music for creators from gaming, media, and other interactive technologies as an example. It uses its sample library and datasets to generate new tracks tailored for any requirements you want. It has been one of the market’s leaders until Shutterstock, a world-leading provider of content for creators, acquitted Amper at the end of 2020.
AIVA, an AI platform that focuses on classical music and boasts of clients like Nvidia, TED, Vodafone, need royalty-free jingles and bespoke pieces of music. Deep learning algorithms fed a lot of orchestral music are under the hood of this platform, and a straightforward user interface is a nice bonus.
Loudly, a start-up from Berlin is also focusing on creating music “Designed by creators, for creators,” as they claim it on the official site. The platform’s system can rework already existing compositions and generate bespoke ones using GAN (Generative Adversarial Network) deep-learning algorithms. Loudly also offers the sample store, music maker app, and a community platform for other creators to collaborate.
AI-composed music is also raising the question of copyright. Who is receiving royalties for the composition, created by AI but trained on the musical data of the artist? Moreover, most jurisdictions, including the US, Spain, and Germany, still state that only human-created works can be protected by copyright. It is time to stock up on popcorn and watch.
AI for music streaming
While many players in the music industry are suffering from the pandemic’s impact, the global music streaming market reached US$21,6 billion in 2020, growing by 7,4%. According to the International Federation of the Phonographic Industry, the global recorded music industry started to recover from its decade-long decline in 2014. The streaming service is one of the primary drivers of the renaissance, given the fact that its share reached 62,1% of total global recorded music revenues in 2020.
Moreover, the overall upward trend for music streaming is global, with Latin America and Asia as the most dynamic markets (30,2% and 29,9% of growth correspondingly). Streaming services are also dominating in Africa and the Middle East markets, not to mention the well-established positions in Europe and North America.
AI, namely machine and deep learning, stands behind all of these with its recommender systems that ensure a smooth customer experience for a listener. By the way, check out our blog Deep Learning Based Recommender Systems for more details.
So, how AI exactly helps music streaming services to indulge their customers? Let us look under the hood of the most significant market shareholder at the moment.
In Q2 2020, 34% of global users opted for Spotify comparing to the closest rival, Apple Music, with 21%. What makes Spotify special? Two magic letters: AI.
Technically, the Spotify stack consists of three layers intertwined with data science and machine learning on each layer:
- Data as the bottom layer includes all user data like demographic’s data, listening habits, and other behavioral data. The more data you have, the better recommendations’ system can produce. As per the moment of writing (April 2021), 60,000 songs are added to Spotify each day.
- Shared models are the middle layer providing info on the user’s affinity (how much they like a particular artist, and what are the most favorite ones), embedding space of similarities (explaining the similarity between two artists, playlist, or tracks), and clustering of items.
- Features are the top layers containing machine learning models using data from the first two layers, catering to the user’s satisfaction while using the app.
Spotify provides a context-aware personalized experience directly from the app’s homepage that considers the device and the music is playing on, current trends, the day of the week and time of the day, etc. It is the result of the Bayesian additive regression trees (BART) system that Spotify is using. BART is a flexible prediction model and machine learning approach applied to prediction and classification problems.
AI-powered music analysis, mastering, and education
AI techniques are not limited only to music composing and paving the way to the listeners via streaming services. Ironically, despite the ungrounded disturbance over AI’s music takeover, this technology applies to limiting music piracy worldwide, sound processing, mastering, and education.
For example, BMAT, a Barcelona-based company helping broadcasters, publishers, and labels track music usage all over the platforms, is using AI technologies to process vast amounts of data. It uses audio fingerprinting technology as a compressed digital summary of audio to identify the similarity of the sound. Machine learning algorithms track the similarity of the compositions, even containing the background noise that has been particularly hard for music tracking technology.
Moreover, AI can help in music mastering, which is still pretty pricey for many creators. For instance, LANDR is claiming to be the creative platform for musicians polishing music with the help of machine learning. The platform is operating on a freemium base, providing complete services under the subscription. The AI-powered mastering engine has been trained on data from many mastered tracks, and the algorithms also are classifying compositions based on the production style.
AI-powered applications for learning to play instruments at your place and your pace are also entering the picture. Musical applications like Yousician and Jamstick are the tools providing instant feedback on your progress while learning to play an instrument.
Since 70% of companies to adopt AI technologies by 2030, the music industry is also going to meet some changes. No worries, AI won’t yet replace the artists in creating music transmitting the sensory experience. We exclusively occupy this domain.
However, AI-powered technologies are already creating royalty-free music for creators, music mastering platforms, educational apps that are providing instant feedback and helps to protect the copyright. Another trend, the development of music streaming platforms generating the majority of revenues in the overall music industry, will continue to grow globally. So, music creators also will be considering algorithms for hitting the playlists of the streaming platforms.