This was the year that gen Z had their "Brat summer", or so we were led to believe. Inspired by the hit album by pop sensation Charli xcx, the trend was seen to embody all the messiness of modern youth: trashy, chaotic and bright green. But on the teenager's social media platform of choice, TikTok, a more sepia music trend has been taking root. Despite having an endless amount of music to pair with their short, scrollable videos, TikTok users have been raiding the back catalogues of artists from yesteryear including Bronski Beat and Sade to soundtrack their posts. This year set a new high for use of old tracks on British TikTok posts, with tunes more than five years old accounting for 19 out of its 50 top tracks this year.

Artificial intelligence is here and it's coming for your jobs. That's, at least, what you might think after considering the ever-growing sophistication of AI-generated music. While the concept of machine-composed music has been around since the 1800s (computing pioneer Ada Lovelace was one of the first to write about the topic), the fantasy has become reality in the past decade, with musicians such as Francois Pachet creating entire albums co-written by AI. Some have even used AI to create'new' music from the likes of Amy Winehouse, Mozart and Nirvana, feeding their back catalogue into a neural network. Even stranger, this July, countries across the world will even compete in the second annual'AI Song Contest', a Eurovision-style competition in which all songs must be created with the help of artificial intelligence. But will this technology ever truly become mainstream?

At AI Music, where our back catalogue of content grows every day, it is becoming increasingly necessary for us to create more intelligent systems for searching and querying the music. One such system for doing that can be dictated by the ability to define and quantify the degree of similarity between songs. The core methodology described here tackles the concept of acoustic similarity. Searching for a song using descriptive tags often introduces the issue of semantic inconsistencies. Tags can be highly subjective by age group, culture, and personal preference of a listener.