Non-continuous to continuous sound detection - changes to model required?


We are developing a sound detection application (on a ESP32) to detect animal sounds. I’m familiar with the code changes required to implement a continuous sound detection scheme & have a working implementation.

My question is, does a sound detection model have to be modified if one wants to change from continuous to non-continuous or vice versa?

Many thanks,


I’ve looked at the difference in the code for




And the difference appears to be the expression in the continuous code:

enable_maf = true

This is referred to in ei_run_classifier.h & there is a reference to

ei_printf(“WARN: run_classifier_continuous, enable_maf is true, but performance calibration is not configured.\n”);
ei_printf(" Previously we’d run a moving-average filter over your outputs in this case, but this is now disabled.\n");
ei_printf(" Go to ‘Performance calibration’ in your Edge Impulse project to configure post-processing parameters.\n");
ei_printf(" (You can enable this from ‘Dashboard’ if it’s not visible in your project)\n");

I don’t see an explicit way of setting this option, in for example, the faucet detection example. Screenshot below.

Is enable_maf set to true when the ‘Averaging window duration’ is greater than 0?


Hi @electronicconsult1_g,

To answer your first question: the model can be the same between run_classifier() and run_classifier_continuous(). The big benefit behind run_classifier_continuous() is that it caches previous slices of your DSP processing (MFE, MFCC, spectrogram) to create a rolling window across time. This saves you from having to create a rolling buffer with raw data (thus saving a good amount of RAM).

To answer your second question: run_classifier_continuous() only enables the moving average filter (MAF) when you have gone through the calibration ( process. You can easily create your own MAF by collecting inference results and averaging them over time.

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