diff --git a/TestSelcall.wav b/TestSelcall.wav
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Binary files /dev/null and b/TestSelcall.wav differ
diff --git a/receiver.py b/receiver.py
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+++ b/receiver.py
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+# Run with "ipython --matplotlib=qt receiver.py <file>.wav"
+#
+from __future__ import print_function
+import sys
+import numpy as np
+from scipy import signal
+from scipy.io.wavfile import read
+from scipy.signal import butter, lfilter
+from math import log10, floor
+
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+FRAME_TIME = 0.04  # Frame time in seconds
+
+TONES = [1124,
+         1197,
+         1275,
+         1358,
+         1446,
+         1540,
+         1640,
+         1747,
+         1860,
+         1981,
+         2110]
+
+ALPHABET = ['1',
+            '2',
+            '3',
+            '4',
+            '5',
+            '6',
+            '7',
+            '8',
+            '9',
+            '0',
+            'E']
+            
+
+reffreq = {}
+reffreq['1'] = 1124
+reffreq['2'] = 1197
+reffreq['3'] = 1275
+reffreq['4'] = 1358
+reffreq['5'] = 1446
+reffreq['6'] = 1540
+reffreq['7'] = 1640
+reffreq['8'] = 1747
+reffreq['9'] = 1860
+reffreq['0'] = 1981
+reffreq['e'] = 2110
+
+FILTER_LEN = 1000  # Samples
+
+
+# Shamelessly lifted from
+# https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass
+def butter_bandpass(lowcut, highcut, fs, order=5):
+    nyq = 0.5 * fs
+    low = lowcut / nyq
+    high = highcut / nyq
+    b, a = butter(order, [low, high], btype='band')
+    return b, a
+
+
+def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
+    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
+    y = lfilter(b, a, data)
+    return y
+
+
+# Tone synthesis
+def note(freq, cycles, amp=32767.0, rate=44100):
+    len = cycles * (1.0/rate)
+    t = np.linspace(0, len, int(len * rate))
+    if freq == 0:
+        data = np.zeros(int(len * rate))
+    else:
+        data = np.sin(2 * np.pi * freq * t) * amp
+    return data.astype(int)
+
+def checktrain(train):
+    # Look for 5 definitive tones in a row
+    for idx,[tone,corr] in enumerate(train):
+        #print(idx,tone,corr)
+        ener = 0
+        val = []
+
+        if(idx>4):
+            for i in range(idx-5,idx):
+                tone,corr = train[i]
+                val.append(tone)
+                ener += corr
+
+            if(ener > 54):
+                print(ener," value:",val)
+
+        
+    
+    return 0
+
+  
+# analyze wav file by chunks
+def receiver(file_name):
+    try:
+        sig_rate, sig_noise = read(file_name)
+    except Exception:
+        print('Error opening {}'.format(file_name))
+        return
+
+    print('file: ', file_name, ' rate: ', sig_rate, ' len: ', len(sig_noise))
+
+    if sig_rate == 44100:
+        decimate = 4  # rate = 11025, Fmax = 5512.5 Hz
+    elif sig_rate == 48000:
+        decimate = 5  # rate = 9600, Fmax = 4800 Hz
+    elif sig_rate == 22050:
+        decimate = 2  # rate = 11025, Fmax = 5512.5 Hz
+    elif sig_rate == 11025:
+        decimate = 1  # rate = 11025, Fmax = 5512.5 Hz
+    else:
+        print('Sample rate {} not supported.'.format(sig_rate))
+        return
+
+    if decimate > 1:
+        sig_noise = signal.decimate(sig_noise, decimate)
+        sig_rate = sig_rate / decimate
+    print('length after decimation: ', len(sig_noise))
+
+    frame_len = int(sig_rate * FRAME_TIME)
+    frames = int(floor((len(sig_noise) / frame_len) + 1))
+
+    sig_noise = butter_bandpass_filter(sig_noise,
+                                       1000,
+                                       2200,
+                                       sig_rate,
+                                       order=8)
+
+    template = []
+    for tone in range(0, len(TONES)):
+        template.append(note(TONES[tone], frame_len, rate=sig_rate))
+
+    # See http://stackoverflow.com/questions/23507217/
+    #         python-plotting-2d-data-on-to-3d-axes
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+
+    y = np.arange(len(TONES))
+    print(' Index     1      2      3      4      5      6      7', end='')
+    print('      8      9      0      E      Avg')
+
+    x = range(0, frames)
+    X, Y = np.meshgrid(y, x)
+    Z = np.zeros((len(x), len(y)))
+     
+     
+    hist = []
+    for frame in range(0, frames):
+
+        beg = frame * frame_len
+        end = (frame+1) * frame_len
+
+        corr = np.zeros(len(TONES))
+
+        for tone in range(0, len(TONES)):
+            corr[tone] = log10(np.abs(signal.correlate(sig_noise[beg:end],template[tone],mode='same')).sum())
+            Z[frame, tone] = corr[tone]
+
+        max1 = 0.0
+        # Find most likely tone in set
+        for tone in range(0, len(TONES)):
+            if corr[tone] > max1:
+                max1 = corr[tone]
+                max1idx = tone
+                
+        hist.append([max1idx,max1])
+
+        print('{0:6d}: '.format(frame), end='')
+        avg = np.mean(corr)
+        for tone in range(0, len(TONES)):
+            if tone == max1idx:
+                print('[{0:2.2f}]'.format(corr[tone]), end='')
+            else:
+                if corr[tone] > 1:
+                    print(' {0:2.2f} '.format(corr[tone]), end='')
+                else:
+                    print('   .   ', end='')
+
+        print(' {0:2.2f}'.format(avg))
+
+    checktrain(hist)
+    ax.plot_surface(X, Y, Z, rstride=1, cstride=1000, color='w', shade=True,
+                    lw=.5)
+    # ax.plot_wireframe(X, Y, Z, rstride=1, cstride=1000, lw=.5)
+
+    ax.set_title(file_name)
+    ax.set_xlabel("Tone")
+    ax.set_ylabel("Frame")
+    ax.set_zlabel("Log Correlation")
+
+    ax.set_zlim(10.0, 15.0)
+    ax.set_ylim(0, frames)
+
+    ax.view_init(30, -130)
+
+    plt.show()
+
+
+if __name__ == "__main__":
+    receiver(sys.argv[1])