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free studio quality noise reduction for music

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free studio quality noise reduction for music

Dear HN, this is Thomas from Tape It. We're releasing our studio quality noise reduction software as a free web app today. You can try it at: https://tape.it/denoiser Listening examples: https://www.youtube.com/watch?v=gRjxY7GTV6Y We've been researching on this for over two years, and will present the paper next week at the Audio Engineering Society conference in NY. You can read about the research here: https://tape.it/research/denoiser Short version: we combined a classic DSP denoiser with a neural network that controls it. Training this it was non-trivial, and we describe in the paper how we did it. Our research website also has a 2min video that summarises the paper. The denoiser only removes stationary noise, like fan noise or amp hums. It works on all kinds of audio, not just speech, and we particularly focused on its applicability in music. Our main goal was to create a denoiser that always works and introduces minimal artefacts. To our knowledge, nobody has built such an automatic high-quality denoiser yet. Please check out the video above, as well as the listening examples on our research page, where we also compare it against other software such as large deep learning models like Facebook's HDemucs, the very popular RNNoise, and non-automatic commercial software like iZotope RX. We believe that this is a really useful piece of software. If you record audio, you can basically always run your raw recordings through our denoiser and assume that they'll sound better afterwards. Try it with your guitars, your vocals or field recordings. Really looking forward to your feedback!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, recordings · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
78%78% 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
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · 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 · Strong signals: audio, introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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