Question/Issue:
I am not getting the correct prediction with Pico microphone MAX9814. I can get the correct result with “Live Classification” from the Edge Impulse platform, but not on the device. Can you please help?
Project ID:
175000
Context/Use case:
This is my code,
#include <stdio.h>
#include "edge-impulse-sdk/classifier/ei_run_classifier.h"
#include "pico/stdlib.h"
#include "analog_microphone.h"
#include "analog_microphone.c"
#include "tusb.h"
// configuration
#define INSIZE 16
const struct analog_microphone_config config = {
// GPIO to use for input, must be ADC compatible (GPIO 26 - 28)
.gpio = 26,
// bias voltage of microphone in volts
.bias_voltage = 1.25,
// sample rate in Hz
.sample_rate = 16000,
// number of samples to buffer
.sample_buffer_size = INSIZE,
};
// variables
int16_t sample_buffer[INSIZE];
volatile int samples_read = 0;
float features[2700];
void on_analog_samples_ready() {
// callback from library when all the samples in the library
// internal sample buffer are ready for reading
samples_read = analog_microphone_read(sample_buffer, INSIZE);
}
int raw_feature_get_data(size_t offset, size_t length, float * out_ptr) {
memcpy(out_ptr, features + offset, length * sizeof(float));
return 0;
}
int main(void) {
// initialize stdio and wait for USB CDC connect
stdio_init_all();
printf("connected\n");
ei_impulse_result_t result = {
nullptr
};
ei_printf("Edge Impulse standalone inferencing (Raspberry Pi Pico)\n");
// initialize the analog microphone
if (analog_microphone_init( & config) < 0) {
ei_printf("analog microphone initialization failed!\n");
while (1) {
tight_loop_contents();
}
}
// set callback that is called when all the samples in the library
// internal sample buffer are ready for reading
analog_microphone_set_samples_ready_handler(on_analog_samples_ready);
// start capturing data from the analog microphone
if (analog_microphone_start() < 0) {
ei_printf("PDM microphone start failed!\n");
while (1) {
tight_loop_contents();
}
}
//if (sizeof(features) / sizeof(float) != EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE)
//{
// ei_printf("The size of your 'features' array is not correct. Expected %d items, but had %u\n",
// EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, sizeof(features) / sizeof(float));
// return 1;
//}
while (1) {
ei_printf("\nStarting inferencing in 2 seconds...\n");
//sleep_ms(3000);
ei_printf("Sampling...\n");
// wait for new samples
while (samples_read == 0) {
tight_loop_contents();
}
// store and clear the samples read from the callback
int sample_count = samples_read;
samples_read = 0;
ei_printf("sample_count returned: %d\n", sample_count);
// loop through any new collected samples
for (int i = 0; i < sample_count; i++) {
float val = sample_buffer[i];
features[i] = val;
}
ei_printf("size of features/floats: %d\n", sizeof(features) / sizeof(float));
ei_printf("EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE: %.3f\n", EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE);
ei_printf("EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME: %.3f\n", EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME);
ei_printf("size of features returned: %d\n", sizeof(features));
ei_printf("size of features returned: %d\n", sizeof(features) / sizeof(features[0]));
//sample_count returned: 16
//size of features/floats: 2700
//EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE: 0.000
//EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME: 0.000
//size of features returned: 10800
//size of features returned: 2700
//run_classifier returned: 0
// the features are stored into flash, and we don't want to load everything into RAM
signal_t features_signal;
features_signal.total_length = sizeof(features) / sizeof(features[0]);
features_signal.get_data = &raw_feature_get_data;
// invoke the impulse
EI_IMPULSE_ERROR res = run_classifier(&features_signal, &result, false);
ei_printf("run_classifier returned: %d\n", res);
if (res != 0)
return 1;
ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
result.timing.dsp, result.timing.classification, result.timing.anomaly);
// print the predictions
ei_printf("[");
for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
ei_printf("%.5f", result.classification[ix].value);
#if EI_CLASSIFIER_HAS_ANOMALY == 1
ei_printf(", ");
#else
if (ix != EI_CLASSIFIER_LABEL_COUNT - 1) {
ei_printf(", ");
}
#endif
}
#if EI_CLASSIFIER_HAS_ANOMALY == 1
printf("%.3f", result.anomaly);
#endif
printf("]\n");
sleep_ms(3000);
}
return 0;
}
