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SwaraNotebook – a notation editor for Indian classical music

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SwaraNotebook – a notation editor for Indian classical music

As an enthusiast of Indian classical music, I needed to write music notations in the traditional typeset format. When I didn't find any existing editors, I developed a Swara Notebook, a mobile focused web app to write North Indian Classical ( Hindustani ) music notations. The notes (called Sargam , similar to Solfege) can be written in English, Devnagri and Bangla scripts. The transcribed song can be played back in 6 different rhythmic cycles ( Taal ) to the accompaniment of the Tabla (a type of drum) or a metronome. Here's an example of a transcribed song https://swaranotebook.com/view/vlB9hVgb5OdKcadbhOOYyzKwvpl2/... Since North Indian classical music is oriented around Ragas (similar to modes in western classical music), the keyboard adapts to the notes of a Raga , making it contextually easier to key in notes. A common question I get: can it handle microtones? I chose to not support microtones and other pitch ornamentation such as glissando (called Meend ) since a mobile interface is not the easiest place to add such details. I also find it a useful tool for ear training, by typing out notations to songs I know, and playing it back to know if I guessed the notes right. It is an open source project written in Clojure/script https://github.com/Studio-kalavati/bandish-editor

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, notes, open · Missing: mac, agents, macos
59%59% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Strong signals: songs · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
21%21% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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