Adding noise, Wiener Filtering, and PESQ working

6th-Semester-Spring-2024/DSP/Labs/FinalProject/adding_noise.py

@@ -0,0 +1,82 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.signal
+from scipy.io import wavfile
+import sounddevice as sd
+import random
+from pesq import pesq
+
+
+
+SIGNAL_PATH = "speechfiles/sp01.wav"
+NOISE_PATH = "noisefiles/white.dat"
+
+
+# Scale the signal to the range [-1,1]
+def normalize_signal(signal):
+    min_amp = np.min(signal)
+    normalized_signal = signal - min_amp
+    max_amp = np.max(normalized_signal)
+    normalized_signal *= 2.0/max_amp
+    normalized_signal -= 1
+    return normalized_signal
+
+
+# Load an audio file from disk
+def load_audiofile(path):
+    sound_data = []
+    sample_rate = 8000
+    # Load .dat files as sound files sampled at 8[kHz]
+    if path[-3:] == "dat":
+        with open(path, "r") as sound_file:
+            sound_data_strings = sound_file.readlines()
+            for data_string in sound_data_strings:
+                sound_data.append(eval(data_string.strip()))
+            sound_data = np.array(sound_data, dtype=np.float64)
+    elif path[-3:] == "wav":
+        sample_rate, sound_data = wavfile.read(path)
+        # Make sure it is nparray of floats (trust me bro, normalizing yells at you if its ints)
+        sound_data = np.array(sound_data, dtype=np.float64)
+    return sample_rate, sound_data
+
+
+# Add noise to a signal with a desired SNR
+def add_noise(signal, noise, snr):
+    len_signal = len(signal)
+    len_noise = len(noise)
+
+    # Get a random crop of the noise to match the length of the signal
+    noise_crop_start = random.randrange(len_noise-len_signal)
+    noise_crop = noise[noise_crop_start:noise_crop_start+len_signal]
+
+    # Calculate the power of the signal and noise
+    noise_power = np.linalg.norm(noise_crop, 2)
+    signal_power = np.linalg.norm(signal, 2)
+
+    # Adjust the noise level to match desired SNR
+    u = 10**(snr/20)
+    desired_noise_power = signal_power/u
+    ratio = desired_noise_power / noise_power
+    noise_crop *= ratio
+
+    noisy_signal = signal + noise_crop
+    return noisy_signal
+
+
+def main():
+    signal_sample_rate, signal_data = load_audiofile(SIGNAL_PATH)
+    noise_sample_rate, noise_data = load_audiofile(NOISE_PATH)
+    assert signal_sample_rate == noise_sample_rate, "Signal and noise sampling rates didn't match."
+    sample_rate = signal_sample_rate
+
+    noisy_signal = add_noise(signal_data, noise_data, 0)
+    filtered_signal = scipy.signal.wiener(noisy_signal)
+
+    print(pesq(sample_rate, signal_data, noisy_signal, mode='nb'))
+    print(pesq(sample_rate, signal_data, filtered_signal, mode='nb'))
+    #sd.play(normalize_signal(noisy_signal), samplerate=sample_rate, blocking=True)
+    #sd.play(normalize_signal(filtered_signal), samplerate=sample_rate, blocking=True)
+
+
+if __name__ == "__main__":
+    main()

6th-Semester-Spring-2024/DSP/Labs/FinalProject/play_sound.py

@@ -1,41 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import scipy.signal
-import sounddevice as sd
-
-
-
-SOUND_PATH = "noisefiles/train.dat"
-
-
-def normalize_signal(signal):
-    min_amp = np.min(signal)
-    normalized_signal = signal - min_amp
-    max_amp = np.max(normalized_signal)
-    normalized_signal *= 2/max_amp
-    normalized_signal -= 1
-    return normalized_signal
-
-
-def load_audiofile(path):
-    sound_data = []
-    sample_rate = 8000
-    if path[-3:] == "dat":
-        with open(SOUND_PATH, "r") as sound_file:
-            sound_data_strings = sound_file.readlines()
-            for data_string in sound_data_strings:
-                sound_data.append(eval(data_string.strip()))
-            sound_data = np.array(sound_data)
-    elif path[-3:] == "wav":
-        sample_rate, sound_data = wavfile.read(path)
-    return sample_rate, sound_data
-
-
-def main():
-    sample_rate, sound_data = load_audiofile(SOUND_PATH)
-    print(sample_rate)
-    sd.play(normalize_signal(sound_data), samplerate=sample_rate, blocking=True)
-
-
-if __name__ == "__main__":
-    main()