They use CREPE via the ml5.js library which offers a variety of ML algorithms for the web. It was a delightful surprise to discover that CREPE is being used to power the pitchDetection function.
You can watch the full video here:
The YouTube coding channel The Coding Train just published its latest Coding Challenge: "Ukulele Tuner with Machine Learning Pitch Detection Model", where they use our CREPE model to build a web-based ukulele tuner! Awesome!
They use CREPE via the ml5.js library which offers a variety of ML algorithms for the web. It was a delightful surprise to discover that CREPE is being used to power the pitchDetection function. You can watch the full video here:
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We're glad to announce the release of two open-source tools and a new dataset developed as part of the SONYC project we hope will be of use to the community:
Scaper: a library for soundscape synthesis and augmentation
AudioAnnotator: a javascript web interface for annotating audio data
URBAN-SED dataset: a new dataset for sound event detection
Further information about scaper, the AudioAnnotator and the URBAN-SED dataset, including controlled experiments on the quality of crowdsourced human annotations as a function of visualization and soundscape complexity, are provided in the following papers: Seeing sound: Investigating the effects of visualizations and complexity on crowdsourced audio annotations M. Cartwright, A. Seals, J. Salamon, A. Williams, S. Mikloska, D. MacConnell, E. Law, J. Bello, and O. Nov. Proceedings of the ACM on Human-Computer Interaction, 1(2), 2017. Scaper: A Library for Soundscape Synthesis and Augmentation J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017. We hope you find these tools useful and look forward to receiving your feedback (and pull requests!). Cheers, on behalf of the entire team, Justin Salamon & Mark Cartwright. â-- |
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