boochow

/*                                                                              
 * File: smplefm.cpp                                                            
 *                                                                              
 * Simple FM Synthesis                                                          
 *                                                                              
 */

#include "userosc.h"

typedef struct State {

boochow

#include "userosc.h"

typedef struct State {
  float w0;
  float phase;
  uint8_t flags;
} State;

enum {
  k_flags_none = 0,

boochow

#include "audio_provider.h"
#include "micro_features/micro_model_settings.h"
#include <Arduino.h>
#include <M5Stack.h>
#include <driver/i2s.h>

#define ADC_INPUT      ADC1_CHANNEL_6 //pin 34, for M5Stack Fire
#define PIN_MICROPHONE 34
#define ADC_OFFSET     (ADC_INPUT * 0x1000 + 0xFFF)
#define BACKLIGHT      32

boochow

#include "command_responder.h"
#include <M5Stack.h>
#include <Avatar.h>

using namespace m5avatar;
Avatar avatar;

void InitResponder() {
  M5.begin();
  M5.Lcd.fillScreen(BLACK);

boochow

[env:m5stack-fire]
platform = espressif32
board = m5stack-fire
framework = arduino
monitor_speed = 115200
build_flags = -DARDUINOSTL_M_H -Ilib/tfmicro/third_party/gemmlowp -Ilib/tfmicro/third_party/flatbuffers/include -Ilib/tfmicro/third_party/kissfft

boochow

#include "output_handler.h"
#include <Arduino.h>
#include <M5Stack.h>

void DrawWing() {
  int x = 60;
  int y = 20;
  int w = 200;
  int h = 200;
  int t = 20;

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void loop() {
  // Attempt to read new data from the accelerometer
  bool got_data = ReadAccelerometer(error_reporter, model_input->data.f,
                                    input_length, should_clear_buffer);
  // Don't try to clear the buffer again
  should_clear_buffer = false;
  // If there was no new data, wait until next time
  if (!got_data) return;
  // Run inference, and report any error
  TfLiteStatus invoke_status = interpreter->Invoke();

boochow

import tensorflow as tf

seq_length = 128

model = tf.keras.Sequential([
      tf.keras.layers.Conv2D(
          8, (4, 3),
          padding="same",
          activation="relu",
          input_shape=(seq_length, 3, 1)),                # output_shape=(batch, 128, 3, 8)

boochow

[env:m5stack-fire]
platform = espressif32
board = m5stack-fire
framework = arduino
monitor_speed = 115200
build_flags = -DARDUINOSTL_M_H -Ilib/tfmicro/third_party/gemmlowp -Ilib/tfmicro/third_party/flatbuffers/include 

boochow

extern TfLiteStatus SetupAccelerometer(tflite::ErrorReporter* error_reporter);
extern bool ReadAccelerometer(tflite::ErrorReporter* error_reporter,
                              float* input, int length, bool reset_buffer);