Hands-on – labeling audio data using a CNN

In this section, we will see how to train the CNN network on audio data and use it to label the audio data.

The following code demonstrates the process of labeling audio data using a CNN. The code outlines how to employ a CNN to label audio data, specifically training the model on a dataset of cat and dog audio samples. The goal is to classify new, unseen audio data as either a cat or a dog. Let’s take the cat and dog sample audio data and train the CNN model. Then, we will send new unseen data to the model to predict whether it is a cat or a dog:

1. Load and pre-process the data: The audio data for cats and dogs is loaded from the specified folder structure using the load_and_preprocess_data function. The load_and_preprocess_data function processes the audio data, converting it into mel spectrograms and resizing them for model compatibility.
2. Split data into training and testing sets: The loaded and pre-processed...