What is a common application of CNNs besides image recognition?

Convolutional Neural Networks (CNNs) are predominantly recognized for their prowess in image recognition; however, their architecture has proven to be remarkably versatile, making them well-suited for various types of data beyond images.

In astronomy, CNNs can analyze vast amounts of data, such as identifying celestial objects or classifying astronomical images, helping researchers to uncover patterns and make discoveries in data-rich environments.

When it comes to audio data processing, CNNs can effectively extract features from audio signals, making them adept at tasks like speech recognition, audio classification, and even music genre classification. The spatial hierarchies in the audio spectrograms can be recognized similarly to how images are processed.

Additionally, CNNs have been utilized in text classification tasks, particularly when text is represented in a spatial format, such as word embeddings. This allows CNNs to capture local patterns in text data, which is helpful in detecting sentiment or categorizing topics in documents.

Therefore, the option indicating that CNNs find applications in astronomy, audio, and text classification accurately reflects their versatility and widespread use across diverse fields. This comprehensive utility underscores why the chosen answer encompasses all these domains.