Hi HN!<p>I used the meow sounds from <a href="https://soundspunos.com/animals/10-cat-meow-sounds.html" rel="nofollow">https://soundspunos.com/animals/10-cat-meow-sounds.html</a>. I expected to see very little variability in the meows, maybe just 4-5 different types for basic emotions. To my surprise, each “cat meow” has astonishingly colorful, complex and unique structure, unlike human vowels that follow a more or less predictable pattern: <a href="https://soundshader.github.io/vowels" rel="nofollow">https://soundshader.github.io/vowels</a>.<p>The algorithm behind these images is fairly simple. It computes FFT to decompose the sound into a set of A·cos(2πwt+φ) waves and drops the phase φ to align all cos waves together. This is known as the auto-correlation function (ACF). Before merging them back, it colorizes each wave using its frequency w: the A notes (432·2ⁿ Hz) become red, C notes - green, E notes - blue, and so on. Finally, it merges the colored and aligned cos waves back, using the amplitude A for color opacity, and renders them in polar coordinates, where the radial coordinate is time.