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The Role of Artificial Intelligence in the Music Industry

Updated: May 3

AI is heavily intertwined with our day to day lives, for better or for worse. As technology continues to grow and improve, the impact of AI on most industries gradually increases.



One of the best known examples of AI is IBM’s Watson- a question-answering computer system capable of answering questions posed in natural language. In 2011, Watson competed on “Jeopardy!” against legendary champions, winning the first place prize of $1 million.


AI also plays a crucial role in the medical field. Whilst we may not be aware of it, AI technologies are currently used for many aspects of our medical treatment. For example, AI systems can evaluate patient health issues, conducting a continuous, in-depth analysis of complex personal medical data. Machine learning algorithms can also analyze the relationship between a particular form of treatment and the symptoms found, in order for them to understand how to improve the treatment.


So what does technology, and particularly AI have to do with the Music Industry?


Let’s take a step back. The delicate relationship between computers in general and the Music industry started probably around 1951, when famous computer scientist Alan Turing recorded the first-ever computer-generated music (prior to any AI capabilities).

As cool as that may be, the relationship between new technological capabilities and the music industry could best be defined as “complicated”. On several occasions, new technological inventions came close to killing the Music Industry. For example, when the radio first came out, or when Napster was introduced to the world - the Music Industry suffered devastating years. Despite the difficulties and adjustments incurred by these changes, however, the Music Industry found a way to persevere and continue to develop.


Despite the differences (and inherent conflicts) between the two, music and tech are best known for their cooperation in three main fields.


The first is making music accessible to the general public in easy and intuitive ways (with the likes of Spotify, Pandora and Youtube).


The second and most talked about field in recent years is the recommendation and suggestions systems for music listeners. These platforms help users get acquainted with new and exciting music, based on their own personal taste.


The third one is using innovative technology to help artists and producers create and implement their musical visions more easily, with ground-breaking tools such as Cubase, Pro Tools, Ableton Live, etc.


Recommendation systems were in many ways the first aspects of music and tech that involved the use of AI capabilities. These systems collect enormous amounts of information about songs and listening habits, suggesting similar and relevant songs to follow up with.


However, despite recent progress, AI hasn’t penetrated intro all aspects of the Music Industry. At MyPart for example, we’ve developed an AI based platform for the sole purpose of song search within the music industry. MyPart takes a very different approach when looking at the problem of song placements and song relevance. We’re able to significantly improve the likelihood of matching any given song with a performing artist or music supervisor on the other end. We do this by looking at the challenge as a Machine Learning Classification Problem. AI could be trained to solve such problems, when given enough examples to learn from. Our ML model analyzes hundreds of harmonic, melodic, lyrical and structural features, and prioritizes the results for music executives according to current project needs and personal preferences, based on a benchmark of musical references that they feed the system with. This way, we can offer music decision-makers and recording artists much better odds at finding the song they’re looking for.


Under the Hood

Every benchmark defined by a music executive (A&Rs, music supervisors, publishers, etc.) defines a scope for the AI to sink its teeth into, in order to discern the lyrical and musical common denominators. Trained on this benchmark, MyPart’s AI can then analyze any given song, and sort it according to ‘relevance’ predictions to each defined benchmark. Results are presented to the music executive in a prioritized queue, on a dedicated MyPart mobile app.


MyPart supports two distinct flows:

  1. Song Discovery within massive Music Publisher catalogues

  2. Song Discovery for the mass audience: the huge pool of undiscovered talent out there. Anyone can now submit a song/lyrics to an artist of their choice on MyPart’s platform. MyPart is officially collecting songs for leading recording artists including Dua Lipa, Charlie Puth, Cardi B, and many more.

At MyPart, we strongly believe that technology- and specifically AI capabilities- will benefit the music industry in many meaningful ways. Our aim is to make the jobs of music industry executives, publishers and performing artists significantly more effective, by helping them focus on relevant content from the ever-growing avalanche of potential songs coming in, whether from current music publishing catalogues or aspiring unsigned artists from around the globe.


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