Signal processing is key to embedded machine learning. It cleans up sensor data, can highlight interesting signals, and drastically reduces the number of features that you pass into a machine learning algorithm - making models run faster and more predictable. To build better audio models, especially for non-voice audio (elephants trumpeting, glass breaking, detecting whether you're in a factory or outside) we've updated the MFE and spectrogram signal processing blocks in Edge Impulse to feature better accuracy, better tweakability, and yet fast enough to run on any typical microcontroller.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/even-better-audio-classification-with-our-new-dsp-blocks