The Importance and Use Case of Converting Audio to Spectrograms: A Deeper Look into CNNs and Classification Tasks

Introduction

Understanding audio data can be challenging without a visual representation. This is where the conversion of audio signals into spectrograms plays an important role, particularly in the world of machine learning and data analysis. Spectrograms transform audio into a visual format that highlights various frequencies over time, enabling the application of image processing techniques like Convolutional Neural Networks (CNNs) for tasks such as sound classification. This article provides a deep dive into the process of converting audio to spectrograms and illustrates how CNNs leverage this data to perform classification tasks.

Introduction to Spectrograms

A spectrogram is a visual representation of the spectrum of frequencies in a sound or other signal as they vary with time. It’s essentially a 2D heatmap, where one axis represents time, the other represents frequency, and the colour intensity represents the signal’s amplitude at a specific frequency and time. This transformation is pivotal for analysing complex audio signals, identifying patterns, and even for building systems that can “understand” or classify sounds, such as voice assistants, environmental noise…