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

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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: https://speakmusic.sonophase.com/ 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!

Visit product View on Hacker NewsJun 27, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apple · Missing: agents, macos, agent

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently, songs · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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