The Basics of Audio Signal Processing

Audio signals and their FFT (or Fast Fourier Transform) representations play a central role in the field of audio recognition. At its core, every audio signal is a complex mixture of frequencies, and understanding this composition is critical to recognizing patterns within the sound. The FFT acts as a mathematical tool that breaks down an audio signal into its component frequencies and presents them in a format that's easier for neural networks to interpret. Just as we convert images into numerical formats for image recognition, we use the FFT to convert audio signals into a spectrum of frequencies. This spectrum becomes the input to the deep learning models. By training our neural networks on these FFT representations, we enable them to identify and differentiate between different sounds, be it music, speech, or ambient noise.

4 Ways to Learn