Whistle-based Authentication

Hacker News

With the rise of more and more clever authentication methods, e.g. fingerprint (Apple Touch ID), facial recognition (http://mashable.com/2013/07/25/facial-recognition-payment) and clicking in pictures (http://pixelpin.co.uk), we thought that why not take this trend to the next level: Whistle-based Authentication: http://popdevelop.com/whistle The project uses HTML5 audio (in Chrome) together with some basic signal processing to detect notes that are whistled in a certain order. Only the order is important, so no need for perfect pitch. And kids, don't use this in anything close to a production environment. This project was developed during the Malmoe Music Hack Weekend 2013 and only supports the two demo users. However feel free to fork the project and add your own users and melodies.

Visit product View on Hacker NewsNov 7, 2013

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

91%91% 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: apple, user, notes · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, 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

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

nativeThis product was originally launched on this platform.

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

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

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

37%37% 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

14%14% 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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