Hello,
I’m a university student currently working on a keyword spotting project targeted for microcontrollers.
I really like the Edge Impulse. It handles the audio preprocessing blocks (like MFE/MFCC) and highly optimizes the final C++ library. However, we realized our research goals require us to have more control and train models on university resources.
I tried using the BYOM workflow, but uploading a custom model seems to strip away the preprocessing block from the pipeline on EI (Our model was, however, trained on MFE spectrogram data downloaded from EI). From my understanding, using EI’s interface as well as the Python SDK with my own model generates a deployment that does not have the preprocessing step included.
Essentially, we would still like to use the preprocessing block, quantization, and deployment from EI, but simply substitute models that are too large to train on EI’s cloud. Is there a nice way to set up such a pipeline or include MFCC/MFE functionality in deployment?
Thanks in advance for any guidance!