{"id":15677038,"url":"https://github.com/jonnor/brewing-audio-event-detection","last_synced_at":"2025-05-06T21:25:33.170Z","repository":{"id":138840528,"uuid":"355675809","full_name":"jonnor/brewing-audio-event-detection","owner":"jonnor","description":"Tracking beer/wine using Audio Event Detection with Machine Learning","archived":false,"fork":false,"pushed_at":"2024-06-16T22:52:47.000Z","size":8840,"stargazers_count":14,"open_issues_count":0,"forks_count":2,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-19T14:58:30.573Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Tracking brewing using Audio Event Detection with Machine Learning\n\nThe fermentation process is critical when brewing alcoholic bewerages such as wine, cider, and beer.\nTo check that the fermentation is progressing OK one should pay attention to the activity of the airlock.\nThis repository shows how one can use Machine Learning to listen and cound the bubble \"plops\" of the airlock,\nto track the fermentation activity.\n\nNote: This is part of a **tutorial** on [Machine Learning for Audio Event Detection](https://github.com/jonnor/machinehearing/tree/master/geekleml2021).\nIt is *not intended* to be a replacement for a proper fermentation tracking system.\nIf you just want something that works for fermentation tracking,\nget a [Plaato Airlock](https://plaato.io/products/plaato-airlock).\n\nOr if you want to build something yourself, consider a [BrewBubbles](https://docs.brewbubbles.com/) bubble counter or a [iSpindle](https://www.ispindel.de) hydrometer.\n\nIf you want to learn about [Machine Learning for Audio]((https://github.com/jonnor/machinehearing)), this is for you!\nThis repository will serve as a simple example of a practical audio ML system,\nusing Audio Event Detection.\nIt should be a good starting point for developing similar application.\n\n![Soundsensing logo](./img/soundsensing-banner.png)\n\nThis project is sponsored by [Soundsensing](https://soundsensing.no)\nprovider of IoT audio sensors with built-in Machine Learning,\nused for Noise Monitoring and Condition Monitoring.\nThe sensors are ideal for continious monitoring of audible noises and events,\nand can perform tasks such as Audio Classification, Audio Event Detection and Acoustic Anomaly Detection.\nTheir sensors can transmit compressed and privacy-preserving spectrograms,\nallowing Machine Learning to be done in the cloud using familiar tools like Python.\nOr models can be deployed onto the sensor itself, for a highly efficient on-edge ML solution.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonnor%2Fbrewing-audio-event-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjonnor%2Fbrewing-audio-event-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonnor%2Fbrewing-audio-event-detection/lists"}