This is a Python script that downloads and extracts the UrbanSound8K dataset, a large dataset of urban sounds. It then preprocesses the data by creating and saving spectrograms for each sound, which are then used to train a machine learning model. The script uses the librosa and cv2 libraries for audio and image processing, and the TensorFlow library for training the model.
The script first downloads the dataset using wget and extracts it using tar. It then imports the necessary libraries, including pandas, numpy, and matplotlib for data analysis, and librosa, cv2, and TensorFlow for audio and image processing and machine learning.
The next step is to create spectrograms for each sound file in the dataset. Spectrograms are a visual representation of the frequencies present in an audio signal, and are commonly used in audio processing tasks. The script uses the librosa library to create spectrograms for each sound file, and saves the resulting image files to disk.
After creating the spectrograms, the script preprocesses the data by resizing and normalizing the images. It then splits the data into training, validation, and testing sets.
Finally, the script trains a machine learning model using the TensorFlow library. The model is a convolutional neural network (CNN) that takes the spectrogram images as input and predicts the class of the sound. The model is trained using the training set and evaluated using the validation and testing sets.