Open Hardware: Arduino, ESP32, and TinyML
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Arduino ☛ Patti Engineering is Arduino Pro’s new System Integrators Partner
We are thrilled to announce an exciting collaboration that is set to revolutionize the landscape of Industry 4.0 digitalization and beyond. Arduino Pro is proud to welcome Patti Engineering into our esteemed family of System Integrators Partners. This partnership marks a significant milestone in our commitment to providing cutting-edge solutions to manufacturers worldwide.
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Arduino ☛ Classifying audio on the GIGA R1 WiFi from purely synthetic data
In order to go from three audio classes: speech, music, and background noise to a complete dataset, Nurgaliyev wrote a simple prompt for ChatGPT that gave directions for creating a total of 300 detailed audio descriptions. After this, he grabbed an NVIDIA Jetson AGX Orin Developer Kit and loaded Meta’s generative AudioCraft model which allowed him to pass in the previously made audio prompts and receive sound snippets in return.
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Hackaday ☛ 1D LED PONG, Arduino-Style
Maybe it’s just us, but isn’t it kind of amazing that in a world of pretty darn realistic games, PONG is still thrilling to play? This 1D implementation by [newsonator] is about as exciting as it gets.
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Hackaday ☛ Take The Minimal Pain Out Of ESP32 Programming
Perhaps without many of us realising it, our single board computers perform the task of making programming their processor or SoC a lot easier. They take care of setting the right lines or commands to put the chip in programming mode, they deal with timings, such that we simply fire our code from our dev environment without having to expend much thought. It’s not as though it’s difficult to program most microcontrollers, but there is usually a procedure to set the chip in programming mode. Tired of pressing buttons to achieve this with the ESP32, [DoganM95] took the time to create an all-in-one USB ESP32 programming board.
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Hackster ☛ TinyML: Audio Classification By Utilizing Synthetic Data
The goal of this project is to demonstrate how we can develop an Audio Classification system that can distinguish between different classes. To achieve this, we will be using OpenAI's ChatGPT, Meta's AudioCraft, Nvidia's Jetson AGX Orin Developer Kit 64 GB andEdge Impulse platform to train our model and then deploy it to an edge device such as Arduino GIGA R1 WiFi.