diff --git a/tests/test_metric.py b/tests/test_metric.py
index 714b789..54408cd 100644
--- a/tests/test_metric.py
+++ b/tests/test_metric.py
@@ -3,8 +3,10 @@
 sys.path.append("/Users/chris/Desktop/Whitebalance/torch-log-wmse-audio-quality")
 import torch
 import numpy as np
+import matplotlib.pyplot as plt
 from torch_log_wmse_audio_quality import LogWMSE
 from torch_log_wmse_audio_quality.utils import calculate_rms
+from torch_log_wmse_audio_quality.freq_weighting_filter import prepare_impulse_response_fft, HumanHearingSensitivityFilter
 
 class TestLogWMSELoss(unittest.TestCase):
     def setUp(self):
@@ -68,7 +70,7 @@ def test_forward(self):
 
     def test_logWMSE_metric_comparison(self):
         """For comparison with the original logWMSE metric implementation in numpy."""
-        audio_lengths = [0.01, 0.1, 0.5, 1.0, 2.0, 10.0, 50.0]  # Different audio lengths
+        audio_lengths = [0.01, 0.1, 0.5, 1.0, 2.0, 10.0]  # Different audio lengths
         for i, audio_length in enumerate(audio_lengths):
             log_wmse_loss = LogWMSE(audio_length=audio_length, sample_rate=44100)
             for j in range(3):
@@ -89,5 +91,46 @@ def test_logWMSE_metric_comparison(self):
 
                     print(f"Test {i}, Subtest {j}, Audio Length: {audio_length}, Loss: {loss}, Seed: {(i+1)*(j+1)}")
 
+class TestFreqWeightingFilter(unittest.TestCase):
+    def setUp(self):
+        # Example audio data, replace with actual audio loading if needed
+        self.plot_output = False
+        self.sample_rate = 44100
+        self.audio_length = 1.7516936
+        t = np.arange(0, int(self.audio_length*self.sample_rate)) / self.sample_rate
+        self.audio = torch.tensor(0.5 * np.sin(2 * np.pi * 440 * t))  # A simple 440 Hz sine wave
+        self.audio = self.audio[None, None, None, :]
+
+    def test_prepare_impulse_response_fft(self):
+        ir = torch.rand(512)  # Example impulse response
+        fft_size = 1024
+        ir_fft = prepare_impulse_response_fft(ir, fft_size)
+        self.assertEqual(ir_fft.shape[-1], fft_size//2+1)
+
+    def test_HumanHearingSensitivityFilter(self):
+        plot_upper_bound = 500
+        hhs_filter = HumanHearingSensitivityFilter(audio_length=self.audio_length, sample_rate=self.sample_rate)
+        # Add zeros at index 50-100 to demonstrate time alignment
+        self.audio[:, :, :, 50:100] = 0
+        self.audio[:, :, :, 101:125] = 0.5
+        self.audio[:, :, :, 126:150] = -0.5
+
+        filtered_audio = hhs_filter(self.audio)
+
+        # Plot the first 1000 samples before and after filtering
+        if self.plot_output:
+            fig, axs = plt.subplots(2, 1, figsize=(12, 8))
+            axs[0].plot(self.audio.squeeze()[:plot_upper_bound])
+            axs[0].set_title(f'Original Audio (First {plot_upper_bound} Samples)')
+            axs[0].set_ylim(-1, 1)
+            axs[1].plot(filtered_audio.squeeze()[:plot_upper_bound])
+            axs[1].set_title(f'Filtered Audio (First {plot_upper_bound} Samples)')
+            axs[1].set_ylim(-1, 1)
+            plt.tight_layout()
+            plt.show()
+
+        self.assertEqual(filtered_audio.shape, self.audio.shape)
+
+
 if __name__ == "__main__":
     unittest.main()
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