If you’ve been around for as long as I have, you would have consumed music in many different ways. I remember listening to my father’s copy of Led Zeppelin III on cassette and Bob Dylan’s The Times They Are a-Changin' on vinyl.
As I grew older, I started buying CDs and even purchased a few minidiscs. The latter started with Nine Inch Nails’ Broken. But all that changed with digital downloads and MP3s.
Back then, I was the guy people reached out to for a copy of a particular song or album because my collection was extensive. But now, I don’t own a single CD or MP3 thanks to streaming services.
The music industry has long been guilty of evolving rapidly, but often, it was at the expense of the audience. Napster was a direct response to that! But things are getting better in the age of data.
Over the past decade, most of us in the western hemisphere have been listening to our favorite artists through platforms like Spotify (who have over 200 million active users). But while the mode of consumption may have changed, the fierce competition within the industry remains the same.
Record companies have to quickly adapt to the listeners’ continuously changing tastes and meet their continually rising expectations. While there were questions about how platforms like Spotify would be able to monetize their offering in 2008, today, it’s the single most significant driver of revenue for the industry.
AI Provides Personalized Recommendations
Streaming services are successful because of user-generated content, carefully curated (and shared) playlists, and personalized recommendations. Artificial Intelligence (AI) got in through the door to enable personalized recommendations, but it has grown to become so much more.
Making recommendations, whether it be on Amazon, Apple Music, or Netflix leverages AI and Machine Learning (ML) to identify patterns in the data and make recommendations. Almost all of us have experienced it and know the true value of this technology. However, this was no easy feat.
Today, AI helps Spotify sort through as much as 20,000 new tracks that are uploaded on to the platform every day. It’s just humanly impossible for us to get through that kind of volume to keep pace with the ever-changing taste of the modern listener.
According to Scott Cohen, Chief Innovation Officer at Warner Music, “every ten years, something kills the music industry. If you want to know what's next look at the tech world." Today, AI has killed off the music genre because AI-driven playlists aren’t based on any genre.
According to Dr. Dorien Herremans, Researcher and Assistant Professor at the Singapore University of Technology and Design, “while humans can rely on their intuitive understanding of musical patterns and the relationships between them, it remains a challenging task for computers to capture and quantify musical structures. Recently, researchers have attempted to use deep learning models to learn features and relationships that allow us to accomplish tasks such as music transcription, audio feature extraction, emotion recognition, music recommendation, and automated music generation.”
But what else can you do with smart algorithms? How else is AI transforming the music business?
AI Enhances User Experience (UX)
AI and ML are also at the core of boosting UX on music streaming platforms. For example, AI is leveraged to improve search engines, increase storage, and promises to take the industry in a whole new direction.
It’s not difficult to make such a prediction if we take a look back at the evolution of the industry. When we had cassettes and CDs, fans had to go out and physically shop for the album before they could listen to it.
The internet era brought about immediate access to MP3s. Now, AI has created a scenario where we don’t even have to search for new music. Instead, we’re exposed to new music and artists that we’re probably going to like. In a way, you can even say that AI is discovering a marketing new talent.
As this technology is still in its infancy, you can also expect it to keep improving UX as it evolves with the platforms themselves.
AI Masters Audio Cost-Effectively
If you’re currently working as a mastering engineer in a recording studio, I have some bad news for you. AI can potentially change the whole vibe in recording studios and make them less crowded.
Today, with AI-driven services like LANDR, you don’t need a mastering engineer to master your recordings. Soon, you might not even need an artist, but I’ll come back to that later.
According to Thomas Birtchnell, a researcher at the University of Wollongong in Australia (and a musician himself), “since the inception of recorded music, there has been a need for standards and reliability across sound formats and listening environments. The role of the audio mastering engineer is prestigious and akin to a craft expert combining scientific knowledge, musical learning, manual precision and skill, and an awareness of cultural fashions and creative labor. With the advent of algorithms, big data and machine learning loosely termed artificial intelligence in this creative sector, there is now the possibility of automating human audio mastering processes and radically disrupting mastering careers.”
This presents an opportunity for up and coming artists because they can now benefit from broadcast quality standards for a fraction of the costs. LANDR’s basic membership costs just $4 a month while their professional offering will only set you back about $25 a month.
AI Makes Music
By now, it probably won’t come as a surprise that AI is also making music. Music composition was one of the first achievements of smart algorithms as far back as the 1950s. Today, although we’re not ready to replace Taylor Swift with a robot (at least not just yet), we’re getting closer.
AI composes music by analyzing data during the composition phase. With the help of reinforcement learning, these smart algorithms can identify the patterns and characteristics that we (humans) would find appealing.
These smart musical algorithms can also combine elements in unique ways and create a sound that we haven’t heard before (check out this video!). As these ML algorithms mature, the music can only get better.