Background noise removal SDK for audio

Hacker News

Hi HN, We built a new SDK for removing background noise in audio: https://getutterly.com. It works much better than the built-in options available in Zoom, Teams and Hangouts and covers a wide range of background noises. Best of all it runs on CPU in real-time, so that you can use it in your audio communication apps. You can check out the noise coverage demo here: https://www.utterly.app/#demo. The integration is simple - you only need two lines of code integrate the SDK into your app. We currently support any audio API, including WebRTC. If you are interested, please feel free to reach out to us!

Visit product View on Hacker NewsAug 30, 2021

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

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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