2e:I[75397,["/_next/static/chunks/0h0py-_snytkj.js","/_next/static/chunks/0iopt1z1og1bl.js","/_next/static/chunks/0ez.9_8dj5uj7.js","/_next/static/chunks/01ynijpvevnb_.js","/_next/static/chunks/0nz80kh6514qf.js","/_next/static/chunks/0ktj73v1.1ksi.js","/_next/static/chunks/0sxgeqcdd6r21.js","/_next/static/chunks/13va_-.agr17v.js","/_next/static/chunks/14zec~t6ef~5_.js","/_next/static/chunks/0ye-ceccmcmyv.js","/_next/static/chunks/0bq-gh~3yglfr.js","/_next/static/chunks/03qgezehmo1v3.js"],"UserView"] 2f:T51b,We’re a group of electronic music artists and PhD researchers working in AI music.

We've been working on a project to help us discover new music in a more objective way. It's called Speak Music:

We’ve trained an AI model to understand the correspondence between music and language. The model combines a machine listening and audio signal processing with transformers for text embeddings. Once trained, we index a huge catalogue of unseen audio, ensuring that the search system can efficiently scale to millions of tracks.

At the moment, our model is optimised for our preferred music; electronic, techno, ambient, dub and relaxing etc. We’re currently fine-tuning to handle all kinds of genres and moods.

Our model enables two types of discovery: (a) natural language search and (b) similarity search.

(a) Speak: search for music using freeform natural language prompts. Describe the mood, aesthetic, texture, setting and context of a track.

(b) Music: discover tracks that are acoustically similar to ANY reference track from Apple Music.

We recently presented at Sonar+D in Barcelona.

We hope you like it. Let us know what you think!

Submissions

Show HN: AI audio embeddings - Discover 100M+ iTunes songs with language models

[untitled]

comments