changed default in wpe command line interface, refactored power estim…

…ation in tf_wpe

examples/WPE_Tensorflow_online.ipynb

@@ -146,7 +146,7 @@
     "    Q_tf = tf.placeholder(tf.complex128, shape=(frequency_bins, channels * taps, channels * taps))\n",
     "    G_tf = tf.placeholder(tf.complex128, shape=(frequency_bins, channels * taps, channels))\n",
     "    \n",
-    "    results = online_wpe_step(Y_tf, get_power_online(Y_tf), Q_tf, G_tf, alpha=alpha, taps=taps, delay=delay)\n",
+    "    results = online_wpe_step(Y_tf, get_power_online(tf.transpose(Y_tf, (1, 0, 2))), Q_tf, G_tf, alpha=alpha, taps=taps, delay=delay)\n",
     "    for Y_step in tqdm(aquire_framebuffer()):\n",
     "        feed_dict = {Y_tf: Y_step, Q_tf: Q, G_tf: G}\n",
     "        Z, Q, G = session.run(results, feed_dict)\n",

nara_wpe/tf_wpe.py

@@ -57,43 +57,106 @@ def _slice(x):
 
 
 def get_power_online(signal):
-    """Calculates power over last to frames for `signal`
+    """Calculates power for `signal`
 
         Args:
-            signal (tf.Tensor): Single frequency signal with shape (T, F, D).
+            signal (tf.Tensor): Signal with shape (F, D, T).
 
         Returns:
-            tf.Tensor: Inverse power with shape (F,)
+            tf.Tensor: Power with shape (F,)
 
     """
-    power_estimate = tf.real(signal) ** 2 + tf.imag(signal) ** 2
-    power_estimate += tf.pad(
-        power_estimate,
-        ((1, 0), (0, 0), (0, 0))
-    )[:-1, :]
-    power_estimate /= 2
-    power_estimate = tf.reduce_mean(power_estimate, axis=(0, -1))
+    power_estimate = get_power(signal)
+    power_estimate = tf.reduce_mean(power_estimate, axis=-1)
     return power_estimate
 
 
-def get_power_inverse(signal, channel_axis=0):
+def get_power_inverse(signal):
     """Calculates inverse power for `signal`
 
     Args:
         signal (tf.Tensor): Single frequency signal with shape (D, T).
-        channel_axis (int): Axis of the channel dimension. Will be averaged.
-
+        psd_context: context for power estimation
     Returns:
         tf.Tensor: Inverse power with shape (T,)
 
     """
-    power = tf.reduce_mean(
-        tf.real(signal) ** 2 + tf.imag(signal) ** 2, axis=channel_axis)
+    power = get_power(signal)
     eps = 1e-10 * tf.reduce_max(power)
     inverse_power = tf.reciprocal(tf.maximum(power, eps))
     return inverse_power
 
 
+def get_power(signal, axis=-2):
+    """Calculates power for `signal`
+
+    Args:
+        signal (tf.Tensor): Single frequency signal with shape (D, T) or (F, D, T).
+        axis: reduce_mean axis
+    Returns:
+        tf.Tensor: Power with shape (T,) or (F, T)
+
+    """
+    power = tf.real(signal) ** 2 + tf.imag(signal) ** 2
+    power = tf.reduce_mean(power, axis=axis)
+
+    return power
+
+
+#def get_power(signal, psd_context=0):
+#    """
+#    Calculates power for single frequency signal.
+#    In case psd_context is an tuple the two values
+#    are describing the left and right hand context.
+#
+#    Args:
+#        signal: (D, T)
+#        psd_context: tuple or int
+#    """
+#    shape = tf.shape(signal)
+#    if len(signal.get_shape()) == 2:
+#        signal = tf.reshape(signal, (1, shape[0], shape[1]))
+#
+#    power = tf.reduce_mean(
+#        tf.real(signal) ** 2 + tf.imag(signal) ** 2,
+#        axis=-2
+#    )
+#
+#    if psd_context is not 0:
+#        if isinstance(psd_context, tuple):
+#            context = psd_context[0] + 1 + psd_context[1]
+#        else:
+#            context = 2 * psd_context + 1
+#            psd_context = (psd_context, psd_context)
+#
+#        power = tf.pad(
+#            power,
+#            ((0, 0), (psd_context[0], psd_context[1])),
+#            mode='constant'
+#        )
+#        print(power)
+#        power = tf.nn.convolution(
+#            power,
+#            tf.ones(context),
+#            padding='VALID'
+#        )[psd_context[1]:-psd_context[0]]
+#
+#        denom = tf.nn.convolution(
+#            tf.zeros_like(power) + 1.,
+#            tf.ones(context),
+#            padding='VALID'
+#        )[psd_context[1]:-psd_context[0]]
+#        print(power)
+#        power /= denom
+#
+#    elif psd_context == 0:
+#        pass
+#    else:
+#        raise ValueError(psd_context)
+#
+#    return tf.squeeze(power, axis=0)
+
+
 def get_correlations(Y, inverse_power, taps, delay):
     """Calculates weighted correlations of a window of length taps
 

nara_wpe/wpe.py

@@ -489,7 +489,7 @@ def get_power_online(signal):
     """
 
     Args:
-        signal : Single frequency signal with shape (F, D, T).
+        signal : Signal with shape (F, D, T).
     Returns:
         Inverse power with shape (F,)
 
@@ -884,8 +884,7 @@ def perform_filter_operation_v5(Y, Y_tilde, filter_matrix):
 )
 @click.option(
     '--file_template',
-    default='AMI_WSJ20-Array1-{}_T10c0201.wav',
-    help='Audio example. Full path required'
+    help='Audio example. Full path required. Included example: AMI_WSJ20-Array1-{}_T10c0201.wav'
 )
 @click.option(
     '--taps_frequency_dependent',