Splash Pro (Gen-2) – Midjourney for music

Hacker News

Hi HN, We recently launched a massive update to our generative music product Splash Pro, which features 100% AI-generated music (the music is entirely composed by neural networks - similar to Stable Audio). Our music was trained on a dataset we've built in-house, so all the outputs are available with a commercial license. Everything you create in Gen-2 is unique (although some musical styles have less training data than others, and you may hear some similarities). Our music is all 44.1kHz and stereo. I have been having an unbelievable amount of fun playing with it. It's especially good at EDM (house, techno, d&b, dubstep etc), but does metal, punk, lo-fi, and other genres quite well. We also have a very clear roadmap ahead of us: AI music tech is going to get rapidly better from here. Here are a few mixes I've put together, using songs from Splash Pro: - Lo-fi mix: https://soundcloud.com/lexandstuff/neural-network-music-lo-f... - 90's Punk medley: https://soundcloud.com/lexandstuff/ai-punk-medley - Tech house mix: https://soundcloud.com/lexandstuff/ai-generate-future-house-... - Neurofunk D&B: https://soundcloud.com/lexandstuff/ai-generated-neurofunk-dn... For producers, the stems are all available to download on the max plan. It is a goldmine for sampling and ideas. I hope you enjoy it and let us know what you think. Lex CTO Splash Music

Visit product View on Hacker NewsSep 26, 2023

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

16%16% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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