Bigger nperseg shows more detail in the spectrogram

audio_to_float/scripts/spectrogram.py

@@ -32,7 +32,9 @@ def update(self, event):
         if len(self.buffer) < self.buffer_len:
             return
         samples = np.asarray(self.buffer)
-        f, t, Sxx = signal.spectrogram(samples, self.sample_rate)
+        # TODO(lucasw) this is hugely inefficient if it is re-calculating
+        # for samples that were processed in previous update.
+        f, t, Sxx = signal.spectrogram(samples, self.sample_rate, nperseg=512)
         # TODO(lucasw) is there a standard spectrogram conversion?
         Sxx = np.log(1.0 + Sxx * 2**16)
         mins = np.min(Sxx)

float_to_audio/scripts/gen_float.py

@@ -12,6 +12,7 @@
 # In order to support this a fifo buffer will store all the samples to be
 # played back, and grow it as needed to support the longest sample received.
 
+import random
 import rospy
 
 from sensor_msgs.msg import ChannelFloat32
@@ -56,6 +57,7 @@ def update(self, event):
                 val += sc * (fr * 4.0 - 1.0)
             else:
                 val += sc * (1.0 - ((fr - 0.5) * 4.0))
+            val += random.random() * 0.2
             self.msg.values[i] = val
             self.counter += 1