diff --git a/6th-Semester-Spring-2024/DSP/Labs/FinalProject/adding_noise.py b/6th-Semester-Spring-2024/DSP/Labs/FinalProject/adding_noise.py
index 59a427b..bfcd53d 100644
--- a/6th-Semester-Spring-2024/DSP/Labs/FinalProject/adding_noise.py
+++ b/6th-Semester-Spring-2024/DSP/Labs/FinalProject/adding_noise.py
@@ -5,11 +5,12 @@ from scipy.io import wavfile
 import sounddevice as sd
 import random
 from pesq import pesq
+import json
 
 
 
-SIGNAL_PATH = "speechfiles/sp01.wav"
-NOISE_PATH = "noisefiles/white.dat"
+#SIGNAL_PATH = "speechfiles/sp01.wav"
+#NOISE_PATH = "noisefiles/white.dat"
 
 
 # Scale the signal to the range [-1,1]
@@ -64,16 +65,53 @@ def add_noise(signal, noise, snr):
 
 
 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
+    noise_paths = ("noisefiles/white.dat", "noisefiles/train.dat", "noisefiles/street.dat", "noisefiles/exhibition.dat")
 
-    noisy_signal = add_noise(signal_data, noise_data, 0)
-    filtered_signal = scipy.signal.wiener(noisy_signal)
+    # Compose signal paths for the 30 sentences
+    signal_paths = []
+    for i in range(1,30+1):
+        signal_paths.append(f"speechfiles/sp{i:02}.wav")
+    
+    # SNR in dB
+    snrs = (0, 10, 20, 30)
+
+    pesqs = {"unfiltered": {}, "filtered": {}}
+
+    for snr in snrs:
+        pesqs["unfiltered"][snr] = {}
+        pesqs["filtered"][snr] = {}
+
+        for noise_path in noise_paths:
+            pesqs["filtered"][snr][noise_path[:-4]] = []
+            pesqs["unfiltered"][snr][noise_path[:-4]] = []
+
+            for signal_path in signal_paths:
+                noise_sample_rate, noise_data = load_audiofile(noise_path)
+                signal_sample_rate, signal_data = load_audiofile(signal_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, snr)
+                filtered_signal = scipy.signal.wiener(noisy_signal)
+            
+                noisy_pesq = pesq(sample_rate, signal_data, noisy_signal, mode='nb')
+                filtered_pesq = pesq(sample_rate, signal_data, filtered_signal, mode='nb')
+
+                pesqs["filtered"][snr][noise_path[:-4]].append(noisy_pesq)
+                pesqs["unfiltered"][snr][noise_path[:-4]].append(filtered_pesq)
+
+    pesqs_json = json.dumps(pesqs, indent=4)
+    with open("pesqs.json", "w") as outfile:
+        outfile.write(pesqs_json)
+    
+    plt.plot(snrs, noisy_pesqs, label="PESQ Unfiltered")
+    plt.plot(snrs, filtered_pesqs, label="PESQ Filtered")
+    plt.xlabel("SNR [dB]")
+    plt.ylabel("PESQ")
+    plt.legend()
+    plt.show()
 
-    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)