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AI · February 3, 2023

IT

I trained an AI model on 120M+ songs from iTunes

Hey HN! I just shipped a project I’ve been working on called Maroofy: https://maroofy.com You can search for any song, and it’ll use the song’s audio to find other similar-sounding music. Demo: https://twitter.com/subby_tech/status/1621293770779287554 How does it work? I’ve indexed ~120M+ songs from the iTunes catalog with a custom AI audio model that I built for understanding music. My model analyzes raw music audio as input and produces embedding vectors as output. I then store the embedding vectors for all songs into a vector database, and use semantic…

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In plain words

Maroofy is a music discovery tool that lets users search for songs by audio similarity. It uses a custom AI model trained on over 120 million songs from iTunes to analyze raw audio and find musically similar tracks. The service stores song data in a vector database and uses semantic search to match songs based on sound characteristics rather than metadata. It's designed for music listeners looking to discover new songs related to ones they already enjoy.

written from the facts on this page · September 2026

From the sources

In the maker’s words, at launch

Hey HN! I just shipped a project I’ve been working on called Maroofy: https://maroofy.com You can search for any song, and it’ll use the song’s audio to find other similar-sounding music. Demo: https://twitter.com/subby_tech/status/1621293770779287554 How does it work? I’ve indexed ~120M+ songs from the iTunes catalog with a custom AI audio model that I built for understanding music. My model analyzes raw music audio as input and produces embedding vectors as output. I then store the embedding vectors for all songs into a vector database, and use semantic search to find similar music! Here are some examples you can try: Fetish (Selena Gomez feat. Gucci Mane) — https://maroofy.com/songs/1563859943 The Medallion Calls (Pirates of the Caribbean) — https://maroofy.com/songs/1440649752 Hope you like it, and would love to hear any questions/feedback/comments! :D

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