Exploring audio data augmentation

Let’s see how to manipulate audio data by adding noise, using NumPy.

Adding noise to audio data during training helps the model become more robust in real-world scenarios, where there might be background noise or interference. By exposing a model to a variety of noisy conditions, it learns to generalize better.

Augmenting audio data with noise prevents a model from memorizing specific patterns in the training data. This encourages the model to focus on more general features, which can lead to better generalization on unseen data:

import numpy as np
def add_noise(data, noise_factor):
    noise = np.random.randn(len(data))
    augmented_data = data + noise_factor * noise
    # Cast back to same data type
    augmented_data = augmented_data.astype(type(data[0]))
    return augmented_data

This code defines a function named add_noise that...