Destructive Commons : Voice / Instrumental automatic separation

Hacker News

What is Destructive Commons? Demixing and sampling. You send the mix as mp3 file, we run a complicated algorithm, and you get separated background and lead tracks as mp3 files. With those, you can do sampling or automatic karaoke. ? Destructive Commons is a wonderful piece of signal processing technology which automatically unmixes an mp3 music file into its vocal and instrumental parts. You upload a mp3 file, you leave your email, and a few minutes later, you get a link for your separated tracks, which is up for a few hours. Does it work? Depends. Basically, the algorithm separates stuff that is repetitive from stuff that is not. Usually, you get vocals and instrumentals as separated tracks. You may end up with some guitar solo or such stuff. Indeed, those are not repetitive neither. Furthermore, separation is not perfect at all and you usually end up with sound artifacts. However, with sufficiently noisy covers or hardcore drums over them, that should be nice. Results depend of the original song, the weather and your cat.

Visit product View on Hacker NewsFeb 6, 2015

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

81%81% 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: para · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

42%42% 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

40%40% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

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