diff --git a/Wave_Mic b/Wave_Mic
new file mode 100644
index 0000000..7b66526
--- /dev/null
+++ b/Wave_Mic
@@ -0,0 +1,105 @@
+#Record your voice and get a picture of the wave form
+import sys
+import pyaudio
+from struct import unpack
+import numpy as np
+from Adafruit_LED_Backpack import BicolorMatrix8x8
+
+# Create BicolorMatrix display instance with default settings
+display = BicolorMatrix8x8.BicolorMatrix8x8()
+display.begin()
+display.clear()
+display.set_brightness(7)
+
+spectrum  = [1,1,1,3,3,3,2,2]
+matrix    = [0,0,0,0,0,0,0,0]
+power     = []
+weighting = [2,8,8,16,16,32,32,64]
+
+def list_devices():
+    # List all audio input devices
+    p = pyaudio.PyAudio()
+    i = 0
+    n = p.get_device_count()
+    while i < n:
+        dev = p.get_device_info_by_index(i)
+        if dev['maxInputChannels'] > 0:
+           print(str(i)+'. '+dev['name'])
+        i += 1
+
+# Audio setup
+no_channels = 1
+sample_rate = 44100
+
+# Chunk must be a multiple of 8
+# NOTE: If chunk size is too small the program will crash
+# with error message: [Errno Input overflowed]
+chunk = 3072
+
+list_devices()
+# Use results from list_devices() to determine your microphone index
+device = 2
+
+p = pyaudio.PyAudio()
+stream = p.open(format = pyaudio.paInt16,
+                channels = no_channels,
+                rate = sample_rate,
+                input = True,
+                frames_per_buffer = chunk,
+                input_device_index = device)
+
+
+# Return power array index corresponding to a particular frequency
+def piff(val):
+    return int(2*chunk*val/sample_rate)
+
+def calculate_levels(data, chunk,sample_rate):
+    global matrix
+    # Convert raw data (ASCII string) to numpy array
+    data = unpack("%dh"%(len(data)/2),data)
+    data = np.array(data, dtype='h')
+ # Apply FFT - real data
+    fourier=np.fft.rfft(data)
+    # Remove last element in array to make it the same size as chunk
+    fourier=np.delete(fourier,len(fourier)-1)
+    # Find average 'amplitude' for specific frequency ranges in Hz
+    power = np.abs(fourier)
+    matrix[0]= int(np.mean(power[piff(0)    :piff(156):1]))
+    matrix[1]= int(np.mean(power[piff(156)  :piff(313):1]))
+    matrix[2]= int(np.mean(power[piff(313)  :piff(625):1]))
+    matrix[3]= int(np.mean(power[piff(625)  :piff(1250):1]))
+    matrix[4]= int(np.mean(power[piff(1250) :piff(2500):1]))
+    matrix[5]= int(np.mean(power[piff(2500) :piff(5000):1]))
+    matrix[6]= int(np.mean(power[piff(5000) :piff(10000):1]))
+    matrix[7]= int(np.mean(power[piff(10000):piff(20000):1]))
+    # Tidy up column values for the LED matrix
+    matrix=np.divide(np.multiply(matrix,weighting),1000000)
+    # Set floor at 0 and ceiling at 8 for LED matrix
+    matrix=matrix.clip(0,8)
+    return matrix
+
+# Main loop
+while 1:
+    try:
+        # Get microphone data
+        data = stream.read(chunk)
+        matrix=calculate_levels(data, chunk,sample_rate)
+        display.clear()
+        for y in range (0,8):
+            for x in range(0, matrix[y]):
+                display.set_pixel(x, y, spectrum[x])
+        display.write_display()
+    except KeyboardInterrupt:
+        print("Ctrl-C Terminating...")
+        stream.stop_stream()
+        stream.close()
+        p.terminate()
+        sys.exit(1)
+    except Exception, e:
+        print(e)
+        print("ERROR Terminating...")
+        stream.stop_stream()
+        stream.close()
+        p.terminate()
+        sys.exit(1)
+