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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Emmi[[:space:]]Elliott[[:space:]]-[[:space:]]Face[[:space:]]to[[:space:]]Face.wav filter=lfs diff=lfs merge=lfs -text

Emmi Elliott - Face to Face.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9f77eb947cff98dce0ad0353faed3ac48566ac77e97ab94117131b55be2bf3d
+size 2911547

Estella.py

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+import os
+import numpy as np
+import librosa
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+import re
+
+# Constants
+SAMPLE_RATE = 44100  # Sample rate
+NUM_MFCC = 13
+MAX_LEN = 1000
+WINDOW_SIZE = 1  # Window size in seconds
+HOP_SIZE = 1   # Hop size (overlap) in seconds
+
+# Function to extract MFCC features
+def extract_features(file_path):
+    y, sr = librosa.load(file_path, sr=SAMPLE_RATE)
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=NUM_MFCC)
+    
+    # Pad or truncate MFCCs to a fixed length
+    if mfccs.shape[1] < MAX_LEN:
+        padding = MAX_LEN - mfccs.shape[1]
+        mfccs = np.pad(mfccs, ((0, 0), (0, padding)), mode='constant')
+    else:
+        mfccs = mfccs[:, :MAX_LEN]
+    
+    return mfccs
+
+# Load dataset
+def load_data(dataset_path):
+    features = []
+    labels = []
+    
+    # Regex pattern to extract class name from filename
+    pattern = re.compile(r'^(.*?)(?: \d+)?\.wav$')
+    
+    for file_name in os.listdir(dataset_path):
+        if file_name.endswith('.wav'):
+            file_path = os.path.join(dataset_path, file_name)
+            match = pattern.match(file_name)
+            if match:
+                label = match.group(1)  # Extract class name without number
+                mfccs = extract_features(file_path)
+                features.append(mfccs)
+                labels.append(label)
+    
+    if len(features) == 0 or len(labels) == 0:
+        raise ValueError("No data found. Ensure the dataset path is correct and contains .wav files.")
+    
+    return np.array(features), np.array(labels)
+
+# Load data
+dataset_path = 'dataset'
+X, y = load_data(dataset_path)
+
+# Encode labels
+label_encoder = LabelEncoder()
+y_encoded = label_encoder.fit_transform(y)
+y_categorical = tf.keras.utils.to_categorical(y_encoded)
+
+# Save LabelEncoder
+np.save('label_encoder.npy', label_encoder.classes_)
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y_categorical, test_size=0.2, random_state=42)
+
+# Build model
+model = Sequential([
+    tf.keras.layers.Input(shape=(NUM_MFCC, MAX_LEN, 1)),
+    Conv2D(32, kernel_size=(3, 3), activation='relu'),
+    MaxPooling2D(pool_size=(2, 2)),
+    Conv2D(64, kernel_size=(3, 3), activation='relu'),
+    MaxPooling2D(pool_size=(2, 2)),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dropout(0.5),
+    Dense(len(np.unique(y_encoded)), activation='softmax')
+])
+
+# Adjust learning rate if necessary
+from tensorflow.keras.optimizers import Adam
+
+learning_rate = 0.0001  # Adjust as necessary
+optimizer = Adam(learning_rate=learning_rate)
+model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Reshape data for the model
+X_train = np.expand_dims(X_train, axis=-1)
+X_test = np.expand_dims(X_test, axis=-1)
+
+# Train model
+history = model.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))
+
+# Save model
+model.save('sound_classification_model.h5')
+
+# Evaluate model
+loss, accuracy = model.evaluate(X_test, y_test)
+print(f"Test accuracy: {accuracy}")
+
+# Function to classify audio in sliding windows with overlapping handling
+def classify_audio(file_path, model, label_encoder, window_size=WINDOW_SIZE, hop_size=HOP_SIZE):
+    y, sr = librosa.load(file_path, sr=SAMPLE_RATE)
+    total_duration = librosa.get_duration(y=y, sr=sr)
+    window_samples = int(window_size * sr)
+    hop_samples = int(hop_size * sr)
+
+    results = []
+    detected_windows = []  # List to keep track of detected windows
+
+    for start in range(0, len(y) - window_samples + 1, hop_samples):
+        end = start + window_samples
+        segment = y[start:end]
+        mfccs = librosa.feature.mfcc(y=segment, sr=sr, n_mfcc=NUM_MFCC)
+
+        # Pad or truncate MFCCs
+        if mfccs.shape[1] < MAX_LEN:
+            padding = MAX_LEN - mfccs.shape[1]
+            mfccs = np.pad(mfccs, ((0, 0), (0, padding)), mode='constant')
+        else:
+            mfccs = mfccs[:, :MAX_LEN]
+
+        mfccs = np.expand_dims(mfccs, axis=0)
+        mfccs = np.expand_dims(mfccs, axis=-1)
+        prediction = model.predict(mfccs)
+        predicted_class = np.argmax(prediction, axis=1)
+        time = start / sr
+        class_label = label_encoder.inverse_transform(predicted_class)[0]
+
+        # Check for overlaps and add detected regions
+        detected = False
+        for (det_start, det_end, det_label) in detected_windows:
+            if (start < det_end and end > det_start):  # Overlapping condition
+                detected = True
+                break
+        
+        if not detected:
+            results.append((time, class_label))
+            detected_windows.append((start, end, class_label))
+
+    return results
+
+# Example usage
+if __name__ == "__main__":
+    # Load model and label encoder
+    def load_model_and_encoder(model_path, label_encoder_path):
+        model = tf.keras.models.load_model(model_path)
+        classes = np.load(label_encoder_path, allow_pickle=True)
+        label_encoder = LabelEncoder()
+        label_encoder.classes_ = classes
+        return model, label_encoder
+
+    model_path = 'sound_classification_model.h5'
+    label_encoder_path = 'label_encoder.npy'
+    audio_path = 'dataset/Debris Wood 02.wav'
+
+    model, label_encoder = load_model_and_encoder(model_path, label_encoder_path)
+
+    sound_identifications = classify_audio(audio_path, model, label_encoder)
+
+    for time, label in sound_identifications:
+        print(f'[{time:.2f} seconds] Class: {label}')

inference.py

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+
+# Example usage
+if __name__ == "__main__":
+    # Load model and label encoder
+    def load_model_and_encoder(model_path, label_encoder_path):
+        model = tf.keras.models.load_model(model_path)
+        classes = np.load(label_encoder_path, allow_pickle=True)
+        label_encoder = LabelEncoder()
+        label_encoder.classes_ = classes
+        return model, label_encoder
+
+    model_path = 'sound_classification_model.h5'
+    label_encoder_path = 'label_encoder.npy'
+    audio_path = 'Emmi Elliott - Face to Face.wav'
+
+    model, label_encoder = load_model_and_encoder(model_path, label_encoder_path)
+
+    sound_identifications = classify_audio(audio_path, model, label_encoder)
+
+    for time, label in sound_identifications:
+        print(f'[{time:.2f} seconds] Class: {label}')

label_encoder.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c20e529c407fd1ab8124e552250df89ef7e0cf8eeb731a178f9da40c8a334b1
+size 98072

script.sh

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+cd dataset
+
+#!/bin/bash
+
+# Loop through all .caf files in the current directory
+for file in *.caf; do
+    # Extract the base name of the file (without extension)
+    base="${file%.*}"
+    # Convert .caf to .wav using ffmpeg
+    ffmpeg -i "$file" "${base}.wav"
+done

sound_classification_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44d94b10c7a04909c1f866e0f502ae3b0649da9d9c8e1e71fd50b9c93fd4e5f1
+size 25801576