diff --git a/train.py b/train.py
index e5fdc12f..f23f3d01 100644
--- a/train.py
+++ b/train.py
@@ -347,9 +347,7 @@ def main(args):
         ref_level_db=c.ref_level_db,
         num_freq=c.num_freq,
         power=c.power,
-        preemphasis=c.preemphasis,
-        min_mel_freq=c.min_mel_freq,
-        max_mel_freq=c.max_mel_freq)
+        preemphasis=c.preemphasis)
 
     # Setup the dataset
     train_dataset = Dataset(
diff --git a/utils/audio.py b/utils/audio.py
index a3805635..51fd3bbf 100644
--- a/utils/audio.py
+++ b/utils/audio.py
@@ -19,8 +19,6 @@ class AudioProcessor(object):
                  num_freq,
                  power,
                  preemphasis,
-                 min_mel_freq,
-                 max_mel_freq,
                  griffin_lim_iters=None):
 
         print(" > Setting up Audio Processor...")
@@ -33,8 +31,6 @@ class AudioProcessor(object):
         self.num_freq = num_freq
         self.power = power
         self.preemphasis = preemphasis
-        self.min_mel_freq = min_mel_freq
-        self.max_mel_freq = max_mel_freq
         self.griffin_lim_iters = griffin_lim_iters
         self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
         if preemphasis == 0:
@@ -54,7 +50,6 @@ class AudioProcessor(object):
         n_fft = (self.num_freq - 1) * 2
         return librosa.filters.mel(
             self.sample_rate, n_fft, n_mels=self.num_mels)
-        #    fmin=self.min_mel_freq, fmax=self.max_mel_freq)
 
     def _normalize(self, S):
         return np.clip((S - self.min_level_db) / -self.min_level_db, 0, 1)
@@ -105,19 +100,6 @@ class AudioProcessor(object):
         else:
             return self._griffin_lim(S**self.power)
 
-    # def _griffin_lim(self, S):
-    #     '''Applies Griffin-Lim's raw.
-    #     '''
-    #     S_best = copy.deepcopy(S)
-    #     for i in range(self.griffin_lim_iters):
-    #         S_t = self._istft(S_best)
-    #         est = self._stft(S_t)
-    #         phase = est / np.maximum(1e-8, np.abs(est))
-    #         S_best = S * phase
-    #     S_t = self._istft(S_best)
-    #     y = np.real(S_t)
-    #     return y
-
     def _griffin_lim(self, S):
         angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
         S_complex = np.abs(S).astype(np.complex)