Created
February 11, 2023 16:01
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| import librosa | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import tensorflow as tf | |
| def plot_spectrogram(sound, sr): | |
| S = librosa.feature.melspectrogram(sound, sr=sr) | |
| log_S = librosa.power_to_db(S, ref=np.max) | |
| plt.figure(figsize=(12, 4)) | |
| librosa.display.specshow(log_S, sr=sr, x_axis='time', y_axis='mel') | |
| plt.colorbar(format='%+2.0f dB') | |
| plt.title('Mel-frequency spectrogram') | |
| plt.tight_layout() | |
| plt.show() | |
| def predict_car_idling(sound, sr): | |
| S = librosa.feature.melspectrogram(sound, sr=sr) | |
| S = np.expand_dims(S, axis=-1) | |
| model = tf.keras.models.Sequential([ | |
| tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(128, 128, 1)), | |
| tf.keras.layers.MaxPooling2D((2,2)), | |
| tf.keras.layers.Conv2D(64, (3,3), activation='relu'), | |
| tf.keras.layers.MaxPooling2D((2,2)), | |
| tf.keras.layers.Flatten(), | |
| tf.keras.layers.Dense(64, activation='relu'), | |
| tf.keras.layers.Dense(2, activation='softmax') | |
| ]) | |
| model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) | |
| prediction = model.predict(S) | |
| idling = np.argmax(prediction) | |
| if idling == 0: | |
| return "Car is idling" | |
| else: | |
| return "Car is not idling" | |
| if __name__ == "__main__": | |
| sound, sr = librosa.load("car_sound.wav") | |
| plot_spectrogram(sound, sr) | |
| result = predict_car_idling(sound, sr) | |
| print(result) |
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