diff --git a/lilfilter/local_amplitude.py b/lilfilter/local_amplitude.py
index 036182b..a917128 100644
--- a/lilfilter/local_amplitude.py
+++ b/lilfilter/local_amplitude.py
@@ -113,7 +113,7 @@ def compute(self,
         # Gaussian smoothing (to correct for end effects)..
         amplitudes = torch.empty(
             (minibatch_size * num_channels + 1), signal_length,
-            dtype=self.dtype)
+            dtype=self.dtype).type_as(input)
 
         # set the last row to all ones.
         amplitudes[minibatch_size*num_channels:,:] = 1
@@ -194,7 +194,7 @@ def _block_sum(self, amplitudes):
         (n, s) = amplitudes.shape
         t = (s + 2 * b - 1) // b
 
-        ans = torch.zeros((n, t), dtype=self.dtype)
+        ans = torch.zeros((n, t), dtype=self.dtype).type_as(amplitudes)
 
         # make sure `amplitudes` is contiguous.
 
diff --git a/lilfilter/resampler.py b/lilfilter/resampler.py
index 7136540..32001b8 100644
--- a/lilfilter/resampler.py
+++ b/lilfilter/resampler.py
@@ -195,7 +195,7 @@ def resample(self, in_data):
             # will be shape (minibatch_size, in_channels, seq_len) with in_channels == 1
             in_data = in_data.unsqueeze(1)
             out = torch.nn.functional.conv1d(in_data,
-                                             self.weights,
+                                             self.weights.type_as(in_data),
                                              stride=self.input_sr,
                                              padding=self.padding)
             # shape will be (minibatch_size, out_channels = 1, seq_len);
@@ -203,7 +203,7 @@ def resample(self, in_data):
             return out.squeeze(1)
         elif self.resample_type == 'integer_upsample':
             out = torch.nn.functional.conv_transpose1d(in_data.unsqueeze(1),
-                                                       self.weights,
+                                                       self.weights.type_as(in_data),
                                                        stride=self.output_sr,
                                                        padding=self.padding)
             return out.squeeze(1)
@@ -221,7 +221,7 @@ def resample(self, in_data):
             # in_channels, width) so we need to reshape (note: time is width).
             in_data = in_data.transpose(1, 2)
 
-            out = torch.nn.functional.conv1d(in_data, self.weights,
+            out = torch.nn.functional.conv1d(in_data, self.weights.type_as(in_data),
                                              padding=self.padding)
             assert out.shape == (minibatch_size, self.output_sr, num_blocks)
             return out.transpose(1, 2).contiguous().view(minibatch_size, num_blocks * self.output_sr)
diff --git a/lilfilter/torch_filter.py b/lilfilter/torch_filter.py
index b19c01f..017b279 100644
--- a/lilfilter/torch_filter.py
+++ b/lilfilter/torch_filter.py
@@ -61,7 +61,7 @@ def apply(self, input):
         # input.unsqueeze(1) changes dim from (minibatch_size, sequence_length) to
         # (minibatch_size, num_channels=1, sequence_length)
         # the final squeeze(1) removes the num_channels=1 axis
-        return torch.nn.functional.conv1d(input.unsqueeze(1), self.filt,
+        return torch.nn.functional.conv1d(input.unsqueeze(1), self.filt.type_as(input),
                                          padding=self.padding).squeeze(1)