old_contents: from __future__ import print_function
  import tensorflow as tf
  from ops import *
  import numpy as np
  
  
  def pre_emph(x, coeff=0.95):
      x0 = tf.reshape(x[0], [1,])
      diff = x[1:] - coeff * x[:-1]
      concat = tf.concat(0, [x0, diff])
      return concat
  
  def de_emph(y, coeff=0.95):
      if coeff <= 0:
          return y
      x0 = y[0]
      x = [x0]
      for n in range(1, y.shape[0], 1):
          new_x = coeff * x[n - 1] + y[n]
          x.append(new_x)
      return np.array(x, dtype=np.float32)
  
  def read_and_decode(filename_queue, canvas_size, preemph=0.):
      reader = tf.TFRecordReader()
      _, serialized_example = reader.read(filename_queue)
      features = tf.parse_single_example(
              serialized_example,
              features={
                  'wav_raw': tf.FixedLenFeature([], tf.string),
                  'noisy_raw': tf.FixedLenFeature([], tf.string),
              })
      wave = tf.decode_raw(features['wav_raw'], tf.int32)
      wave.set_shape(canvas_size)
      wave = (2./65535.) * tf.cast((wave - 32767), tf.float32) + 1.
      noisy = tf.decode_raw(features['noisy_raw'], tf.int32)
      noisy.set_shape(canvas_size)
      noisy = (2./65535.) * tf.cast((noisy - 32767), tf.float32) + 1.
  
      if preemph > 0:
          print('Applying pre-emphasis coef ', preemph)
          wave = tf.cast(pre_emph(wave, preemph), tf.float32)
          noisy = tf.cast(pre_emph(noisy, preemph), tf.float32)
  
      return wave, noisy
  
new_contents: from __future__ import print_function
  import tensorflow as tf
  from ops import *
  import numpy as np
  
  
  def pre_emph(x, coeff=0.95):
      x0 = tf.reshape(x[0], [1,])
      diff = x[1:] - coeff * x[:-1]
      concat = tf.concat(0, [x0, diff])
      return concat
  
  def de_emph(y, coeff=0.95):
      if coeff <= 0:
          return y
      x = np.zeros(y.shape[0], dtype=np.float32)
      x[0] = y[0]
      for n in range(1, y.shape[0], 1):
          x[n] = coeff * x[n - 1] + y[n]
      return x
  
  def read_and_decode(filename_queue, canvas_size, preemph=0.):
      reader = tf.TFRecordReader()
      _, serialized_example = reader.read(filename_queue)
      features = tf.parse_single_example(
              serialized_example,
              features={
                  'wav_raw': tf.FixedLenFeature([], tf.string),
                  'noisy_raw': tf.FixedLenFeature([], tf.string),
              })
      wave = tf.decode_raw(features['wav_raw'], tf.int32)
      wave.set_shape(canvas_size)
      wave = (2./65535.) * tf.cast((wave - 32767), tf.float32) + 1.
      noisy = tf.decode_raw(features['noisy_raw'], tf.int32)
      noisy.set_shape(canvas_size)
      noisy = (2./65535.) * tf.cast((noisy - 32767), tf.float32) + 1.
  
      if preemph > 0:
          print('Applying pre-emphasis coef ', preemph)
          wave = tf.cast(pre_emph(wave, preemph), tf.float32)
          noisy = tf.cast(pre_emph(noisy, preemph), tf.float32)
  
      return wave, noisy
  
subject: work de_emph in numpy directly