added some tdoa stuff

audiphil.py

@@ -2,6 +2,7 @@
 
 from collections import defaultdict
 import audio_features as fea
+import tdoa
 import numpy as np
 import itertools
 import file_processing as iop
@@ -16,13 +17,13 @@ def split_into_channels(sig, num_chan=2):
         for j in xrange(num_chan):
             outputs[j].append(sig[i+j])
 
-    return [a(outputs[x]) for x in outputs]
+    return [np.array(outputs[x]) for x in outputs]
 
 def play_signal(signal, fs = 44100, max_vol=None):
     if max_vol is not None:
-	signal = np.array(signal).copy()
-	signal = signal/max(np.abs(signal))
-	signal = signal*max_vol
+        signal = np.array(signal).copy()
+        signal = signal/max(np.abs(signal))
+        signal = signal*max_vol
     scikits.audiolab.play(signal, fs=fs)
 
 
@@ -64,14 +65,16 @@ def plot_confusion_matrix(cm, classes,
 
     thresh = cm.max() / 2.
     for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
-	if normalize:
-	   plt.text(j, i, "%0.2f"% cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
-	else:
-	   plt.text(j, i, "%d"% int(cm[i, j]), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
+        if normalize:
+            plt.text(j, i, "%0.2f"% cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
+        else:
+            plt.text(j, i, "%d"% int(cm[i, j]), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
 
     plt.tight_layout()
     plt.ylabel('True label')
     plt.xlabel('Predicted label')
     plt.show()
 
 
+
+

ml_au.py

@@ -23,13 +23,13 @@ def kfold(X, y, clfs_names, num_splits, cm=False, cm_size=15):
 
             #cur_acc = f1_score(y_test, y_res, average="macro")
             cur_acc = accuracy_score(y_test, y_res)
-		
-	    if cm:
-		mycm = confusion_matrix(y_test, y_res, labels=labels)
-		au.plot_confusion_matrix(mycm, classes=labels, normalize=False, title="{} {}".format(name, "%0.2f" % accuracy_score(y_test, y_res)), cm_size=cm_size)
+
+            if cm:
+                mycm = confusion_matrix(y_test, y_res, labels=labels)
+                au.plot_confusion_matrix(mycm, classes=labels, normalize=False, title="{} {}".format(name, "%0.2f" % accuracy_score(y_test, y_res)), cm_size=cm_size)
 
             scores.append(cur_acc)
-	    classifiers.append(cur)  
+            classifiers.append(cur)
 
         scores = np.array(scores)
         d[name].append("KFOLD: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

tdoa.py

@@ -0,0 +1,91 @@
+import numpy as np
+
+def Gxx(s1, s2):
+    pad1 = np.zeros(len(s1)).astype("float")
+    pad2 = np.zeros(len(s2)).astype("float")
+
+    cs1 = np.hstack([s1,pad1])
+    cs2 = np.hstack([pad2,s2])
+
+    f_s1 = np.fft.fft(cs1)
+    f_s2 = np.fft.fft(cs2)
+
+    f_s2c = np.conj(f_s2)
+    f_s = f_s1 * f_s2c
+
+    return f_s
+
+def corr(s1, s2, maxdiff=None):
+    f_s=Gxx(s1,s2)
+
+    res = np.fft.ifft(f_s).real
+
+    return np.argmax(res) - len(s2)
+
+def corr_PHAT(s1, s2, maxdiff=None):
+    f_s = Gxx(s1, s2)
+
+    kf_s = f_s/abs(f_s)
+
+    res = np.fft.ifft(kf_s).real
+
+    return np.argmax(res) - len(s2)
+
+def corr_weiner(s1, s2, maxdiff=None):
+
+    f_s = Gxx(a(s1), a(s2))
+    f_s_1 = Gxx(a(s1), a(s1))
+    f_s_2 = Gxx(a(s2), a(s2))
+
+    c12 = (f_s)**2/(f_s_1*f_s_2)
+    kf_s = f_s * abs(c12)
+
+    res = (np.fft.ifft(kf_s)).real
+
+    return np.argmax(res) - len(s2)
+
+def asdf(s1, s2, N=64, maxdiff=None):
+    res = (-2*np.fft.ifft(np.fft.fft(s1)*np.conj(np.fft.fft(s2))).real + sum(s1**2) + sum(s2**2))/N;
+    return np.argmin(res) - len(s2)
+
+def tde_lms(s1, s2, maxdiff=16, _mu=1e-4):
+
+    cfilt = np.zeros(2*maxdiff)
+
+    for i in xrange(maxdiff, len(s1) - maxdiff):
+	x1 = a(s2[i-maxdiff:i+maxdiff])
+
+	err = s1[i] - np.dot(cfilt, x1)
+	cfilt = cfilt + _mu*err*x1
+
+    return np.argmax(cfilt) - maxdiff
+
+def tde_aed(s1, s2, maxdiff=16, _mu=1e-3):
+
+    h0 = np.full(maxdiff, 0.5).T
+    h1 = np.full(maxdiff, 0.5).T
+
+    h0[maxdiff//2] = 1
+    h1[maxdiff//2] = 1
+
+    u = a([h1.T, -h0.T]).T
+
+    for i in xrange(maxdiff, len(s1)):
+	x0 = a([ s1[i-k] for k in xrange(maxdiff)]).T
+	x1 = a([ s2[i-k] for k in xrange(maxdiff)]).T
+
+	xk = a([x0.T, x1.T]).T
+
+	ek = np.dot(u.T,xk)
+
+	t = u - _mu*np.dot(xk, ek)
+	u = (t/np.linalg.norm(t))
+
+    h1 = u[:, 0]
+    h0 = u[:, 1]
+
+    dif = np.argmax(h1) - np.argmax(h0)
+
+    return dif
+
+