# Mulan Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/mulan
> Markdown URL: https://aitinkerers.org/technologies/mulan.md
> Technology record last updated: 2026-04-28T00:26:15Z
> Generated: 2026-09-21T19:40:10Z

Mulan is a joint acoustic-semantic embedding model that links music recordings to natural language descriptions using a dual-encoder architecture.

Developed by Google Research, Mulan maps unaligned audio and text into a shared 128-dimensional embedding space. The model leverages two distinct towers (a ResNet-50 for audio and a BERT-base for text) trained on 44 million music clips and 370,000 hours of audio. By utilizing contrastive learning, Mulan enables zero-shot music tagging and cross-modal retrieval without requiring manual annotations. This technology powers advanced music understanding tasks, allowing systems to identify complex genres or moods (e.g., 'lo-fi hip hop for studying') directly from raw waveforms.

- Official technology site: https://google-research.github.io/seanet/mulan/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [In-Context Learning for Extreme Multi-Label Classification](https://chicago.aitinkerers.org/talks/rsvp_y-7-uutZyio)

How to leverage large language models to identify > 10,000 different labels with < 10 labelled examples for each class? And how to do it by systematically searching the prompt space without tinkering with prompts for each class? There is this Stanford paper (From Chris Potts' group) and an accompanying Github repository that offers an answer to the questions above. I have been tinkering with it. The idea was to share what I am learning with fellow tinkerers in a quick talk.

- Event context: AI Tinkerers Chicago July Meetup — 2024-07-23 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_y-7-uutZyio

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