Merge branch 'stgloor/mic'

Signed-off-by: Stefan Gloor <code@stefan-gloor.ch>

.gitignore

@@ -9,3 +9,7 @@ ml/sample_input.*
 ml/data
 ml/venv
 src/models
+__pycache__
+venv
+tools/*.wav
+tools/*.log

src/dfsdm.c

@@ -45,11 +45,11 @@ void dfsdm_init(void)
 {
 	hdfsdm1_filter0.Instance = DFSDM1_Filter0;
 	hdfsdm1_filter0.Init.RegularParam.Trigger = DFSDM_FILTER_SW_TRIGGER;
-	hdfsdm1_filter0.Init.RegularParam.FastMode = ENABLE;
+	hdfsdm1_filter0.Init.RegularParam.FastMode = DISABLE;
 	hdfsdm1_filter0.Init.RegularParam.DmaMode = ENABLE;
-	hdfsdm1_filter0.Init.FilterParam.SincOrder = DFSDM_FILTER_SINC3_ORDER;
-	hdfsdm1_filter0.Init.FilterParam.Oversampling = 200;
-	hdfsdm1_filter0.Init.FilterParam.IntOversampling = 1;
+	hdfsdm1_filter0.Init.FilterParam.SincOrder = DFSDM_FILTER_SINC2_ORDER;
+	hdfsdm1_filter0.Init.FilterParam.Oversampling = 25;
+	hdfsdm1_filter0.Init.FilterParam.IntOversampling = 4;
 	if (HAL_DFSDM_FilterInit(&hdfsdm1_filter0) != HAL_OK) {
 		ERR("Failed to initialize DFSDM filter 0\n");
 	}
@@ -67,8 +67,8 @@ void dfsdm_init(void)
 		DFSDM_CHANNEL_SPI_CLOCK_INTERNAL;
 	hdfsdm1_channel2.Init.Awd.FilterOrder = DFSDM_CHANNEL_FASTSINC_ORDER;
 	hdfsdm1_channel2.Init.Awd.Oversampling = 1;
-	hdfsdm1_channel2.Init.Offset = -190;
-	hdfsdm1_channel2.Init.RightBitShift = 8;
+	hdfsdm1_channel2.Init.Offset = 0;
+	hdfsdm1_channel2.Init.RightBitShift = 2;
 	if (HAL_DFSDM_ChannelInit(&hdfsdm1_channel2) != HAL_OK) {
 		ERR("Failed to initialize DFSDM channel 2\n");
 	}

src/mic.cc

@@ -53,19 +53,6 @@ void speech::mic::init()
 		ERR("malloc failed.\n");
 	}
 
-	/* Memory test */
-	memset(this->buf, 42, this->buf_size * sizeof(int32_t));
-	for (size_t i = 0; i < this->buf_size; i++) {
-		this->buf[i] = 42;
-	}
-
-	for (size_t i = 0; i < this->buf_size; i++) {
-		if (this->buf[i] != 42) {
-			printf("mismatch on %u\n", i);
-		}
-		assert(this->buf[i] == 42);
-	}
-
 	if (HAL_DFSDM_FilterRegularStart_DMA(&hdfsdm1_filter0, this->buf,
 					     this->buf_size) != HAL_OK) {
 		ERR("Failed to start conversion.\n");

tools/plot.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+import matplotlib.pyplot as plt
+
+def read_data(file_path):
+    data = []
+    with open(file_path, 'r') as file:
+        for line_number, line in enumerate(file, start=1):
+            if line_number >= 5:
+                try:
+                    data.append(float(line.strip()))
+                except ValueError:
+                    print(f"Error parsing line {line_number}: {line}")
+    return data
+
+def plot_data(data):
+    x = list(range(1, len(data) + 1))  # Sample numbers from 1 to 1600
+    plt.figure(figsize=(12, 6))  # Wide figure
+    x = [ i*0.0625 for i in x ]
+    plt.plot(x, data, color='black', linewidth=0.5)  # Thin black line
+    plt.scatter(x, data, color='red', s=3)  # Little red points at each sample
+    #plt.xlabel('Sample Number')
+    plt.xlabel('Time [ms]')
+    plt.ylabel('Amplitude')
+    plt.title('Microphone (16 kS/s)')
+    plt.grid(True)
+    #plt.savefig('plot.pdf')  # Save plot to PDF
+    plt.show()
+
+if __name__ == "__main__":
+    file_path = "data.log"
+    data = read_data(file_path)
+    plot_data(data)

tools/replay.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+
+from plot import plot_data
+
+import simpleaudio as sa
+import numpy as np
+import wave
+import math
+
+sampling_rate = 16000
+resolution = 255
+
+def read_data(file_path):
+    data = []
+    with open(file_path, 'r') as file:
+        for line_number, line in enumerate(file, start=1):
+            if line_number >= 5:
+                try:
+                    data.append(float(line.strip()))
+                except ValueError:
+                    print(f"Error parsing line {line_number}: {line}")
+    # Strip first ~100 ms
+    start_time = 0.100
+    data = data[int(start_time/(1/sampling_rate)):]
+    return data
+
+def record_file(file_path, data, sampling_rate):
+    with wave.open(file_path, mode="wb") as wav_file:
+        wav_file.setnchannels(1)
+        wav_file.setsampwidth(1)
+        wav_file.setframerate(sampling_rate)
+        wav_file.writeframes(bytes(data))
+
+def play_file(file_path):
+    wave_obj = sa.WaveObject.from_wave_file(file_path)
+    play_obj = wave_obj.play()
+    play_obj.wait_done()
+
+if __name__ == "__main__":
+    file_path = "data.log"
+    data = read_data(file_path)
+
+    # Known-good signal
+    #data = []
+    #for i in np.arange(6400):
+    #    time = 1/sampling_rate * i
+    #    amplitude = math.sin(2 * math.pi * 440 * time)
+    #    data.append(amplitude)
+
+    # Normalize data
+    min_val = np.min(data)
+    max_val = np.max(data)
+    data = (data - min_val) / (max_val - min_val)
+    data = [ int(i) for i in (data * resolution) ]
+
+    plot_data(data)
+    record_file("output.wav", data, sampling_rate)
+    play_file("output.wav")