I recently came across an interesting project that combines edge computing with audio processing using the nRF52 and nRF53 series. It’s fascinating to see how low-power devices can handle machine learning tasks like mono audio processing. The example provided, detecting woodpeckers, is a great demonstration of how edge computing can be applied in real-world scenarios.
The setup involves using an external microphone shield with the nRF52840 DK or nRF5340 DK, which doesn’t have a built-in microphone. The project uses the Edge Impulse platform to train and deploy a model that detects woodpecker sounds. The data is collected and processed on the device, which is a great way to save resources and reduce latency.
One thing I found particularly interesting is the workaround for the sample rate mismatch between the DMIC sample and the Edge Impulse wrapper. By running the sampling 11 times to generate a full second of data, they ensure compatibility with the Edge Impulse model. This kind of creative problem-solving is what makes working with edge computing so rewarding.
I’m curious to try this out myself. I wonder if there are other audio-based applications that could benefit from this approach, like detecting specific sounds in a smart home environment. It would also be interesting to explore how the accuracy of the model improves with more training data.
If anyone has experience with similar projects or tips for getting started with Edge Impulse, I’d love to hear about it! The possibilities for integrating audio processing into smart devices seem endless, and I’m excited to dive deeper into this topic.