Building an AI DJ: What I Got Wrong About Music Embeddings

It All Begins With a Vibe Gripe We have all been there, at weddings, maybe even our own. The DJ decides to whip out Cupid Shuffle, Cha-Cha slide, and Cotten-eyed Joe (no offense if you genuinely enjoy these tracks). The intent is to drive engagement, but one thing we have found socially is that obligatory engagement isn't engagement. The purpose of a wedding reception is to get people dancing and to provide them with a great time. Base Premise of the Application I had a hunch that using meta-data, embeddings, personal data, location, and weather I could build an algorithm that outperformed 95% of wedding DJs. I am not bullish that computers will at all replace true art -- Tiesto, Steve Aoki, and Diplo are definitely safe IMO. The goal here is to build something that is budget friendly for couples who don't have an endless budget and want their guests to have a good time. At the end of the day, engagement and music itself can be represented with tags and math. Initial thoughts I initial