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audio_classifier.py

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+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# Le nombre de classes est tiré de ton dataset.
+NUM_CLASSES = 2 
+
+class AudioClassifier(nn.Module):
+    """
+    Réseau de Neurones Convolutionnels (CNN) simple pour la classification audio.
+    C'est l'architecture que nous avons entraînée from scratch.
+    """
+    def __init__(self):
+        super(AudioClassifier, self).__init__()
+        
+        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=(5, 5), padding=2)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.pool1 = nn.MaxPool2d(kernel_size=(2, 2))
+
+        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), padding=1)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.pool2 = nn.MaxPool2d(kernel_size=(2, 2))
+
+        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), padding=1)
+        self.bn3 = nn.BatchNorm2d(128)
+        self.pool3 = nn.MaxPool2d(kernel_size=(2, 2))
+        
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+        self.fc1 = nn.Linear(128 * 1 * 1, NUM_CLASSES) 
+
+    def forward(self, x):
+        x = self.pool1(F.relu(self.bn1(self.conv1(x))))
+        x = self.pool2(F.relu(self.bn2(self.conv2(x))))
+        x = self.pool3(F.relu(self.bn3(self.conv3(x))))
+        
+        x = self.avgpool(x)
+        
+        x = torch.flatten(x, 1)
+
+        return self.fc1(x)