sample recorder

src/record_sound.py

@@ -1,53 +1,69 @@
-# import sounddevice as sd
-# from scipy.io.wavfile import write
-# import scipy
-# import time
-
-# fs = 44200  # Sample rate
-
-# snippets = []
-# for i in range (5):
-#   seconds = 1  # Duration of recording
-#   myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
-#   snippets.append(myrecording)
-#   sd.wait()
-
-# for i in range (5):
-#   write('output{}.wav'.format(i), fs, snippets[i])
-#   sd.play(c, fs)
-#   sd.wait()
-
-from scipy.io.wavfile import read
-import numpy as np
 from numpy import*
+from scipy.io.wavfile import read
+from scipy.io.wavfile import write
 import matplotlib.pyplot as plt
-a=read("/home/jreinking/Projekte/doorbell/raspberry-pi-projects/src/output2.wav")
+import scipy
-print(a)
+import sounddevice as sd
-# plt.plot(list(map(lambda x: x[0], a[1])))
+import time
-# plt.show()
+
+a = read("/home/jreinking/Projekte/doorbell/raspberry-pi-projects/res/klingel_aufnahme_microphne_cut.wav")
+# print(a)
+# print(len(a[1]))
+a = a[1]
+
+def chunks(lst, n):
+  """Yield successive n-sized chunks from lst."""
+  for i in range(0, len(lst), n):
+    yield lst[i:i + n]
+
+
+def compress(a):
+  a = chunks(a, 700)
+  a = list(map(lambda x : mean(x), a))
+  return a
 
 def dtw(s, t):
-    n, m = len(s), len(t)
+  n, m = len(s), len(t)
-    dtw_matrix = np.zeros((n+1, m+1))
+  dtw_matrix = np.zeros((n+1, m+1))
-    for i in range(n+1):
+  for i in range(n+1):
-        for j in range(m+1):
+    for j in range(m+1):
-            dtw_matrix[i, j] = np.inf
+      dtw_matrix[i, j] = np.inf
-    dtw_matrix[0, 0] = 0
+  dtw_matrix[0, 0] = 0
-    
+  
-    for i in range(1, n+1):
+  for i in range(1, n+1):
-        for j in range(1, m+1):
+    for j in range(1, m+1):
-            cost = abs(s[i-1] - t[j-1])
+      cost = abs(s[i-1] - t[j-1])
-            # take last min from a square box
+      # take last min from a square box
-            last_min = np.min([dtw_matrix[i-1, j], dtw_matrix[i, j-1], dtw_matrix[i-1, j-1]])
+      last_min = np.min([dtw_matrix[i-1, j], dtw_matrix[i, j-1], dtw_matrix[i-1, j-1]])
-            dtw_matrix[i, j] = cost + last_min
+      dtw_matrix[i, j] = cost + last_min
-    return dtw_matrix
+  return dtw_matrix
 
-m = dtw(list(map(lambda x: x[0], a[1])), list(map(lambda x: x[0], a[1])))
+# m = dtw(a, a)
-print(m)
+# print(m)
+
+NUMBER_OF_SNIPPETS = 5
+SAMPLE_RATE = 44200
+SECONDS = (400000 / SAMPLE_RATE) / NUMBER_OF_SNIPPETS
+
+snippets = []
+i = 0
+while True:
+  myrecording = sd.rec(int(SECONDS * SAMPLE_RATE), samplerate=SAMPLE_RATE, channels=2)
+  snippets.append(myrecording)
+  sd.wait()
+
+  if len(snippets) > NUMBER_OF_SNIPPETS:
+    snippets = snippets[1:]
+    c = scipy.vstack(snippets)
+    write("sample_{}.wav".format(i), SAMPLE_RATE, c)
+    i += 1
+    #c = compress(c)
+    #plt.plot(c)
+    #plt.show()
+    # print(c)
+    # print(len(list(map(lambda x : x[0], c))))
+    # sd.play(c, SAMPLE_RATE)
+    # sd.wait()
 
 
 
-# print('======')
-
-# c = scipy.vstack((snippets[0], snippets[1], snippets[2], snippets[3], snippets[4]))
-