Audio augmentation is challenging to explain in a book format. Still, we gain a deeper understanding of audio amplitude, frequency, and sampling rate with additional visualization techniques, such as the audio Spectrogram, Mel-spectrogram, and Chroma STFT. Furthermore, in the Python Notebook, you can listen to the before-and-after effects of the audio augmentation.
Compared to the previous chapter, Waveform graphs show the amplitude of a signal over time, giving an understanding of its shape and structure. Spectrogram graphs show a visual representation of the frequencies of a signal over time, providing a deeper insight into the harmonic content of the sound.
An Audio Spectrogram comes in many variations, whether color mapping, window filtering, spectrum sides, magnitude mode, or frequency scale, among many more in the underlying Matplotlib
specgram() function. Pluto uses Python code in wrapper functions on a few Spectrogram types. The majority of Spectrogram variations...