https://github.com/soundshader/soundshader.github.io/blob/master/acf-v2/README.md

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 Visual Morphology of Vowels

Demo: soundshader.github.io/acf-v2

Images below are auto-correlation spectrograms of vowels.

  * Definition of auto-correlation: ACF(X)=FFT[abs(FFT(X))^2]. It
    splits input X into amp*cos(freq*t+phi) waves, drops phases phi
    and squares amplitudes amp. For this reason, ACF(X) is a
    symmetric function.
  * The ACF images below render abs(ACF(X))/max(abs(ACF(X))). The ACF
    values aren't squared and aren't log10-scaled.
  * Low frequencies and high frequencies are rendered with different
    colors by applying bandpass filters: FFT[BPF*abs(FFT(X))^2]. The
    low frequency ACF is rendered with color (12,3,1) and the high
    frequency ACF - with color (1,3,12). Oversaturation allows to
    reveal more details without resorting to log10-scaling (which
    doesn't look good).
  * Sample rate: 16 kHz. Frame size: 4096. The rule of thumb: frame
    size = 1/4 of sample rate. This means that one ACF frame captures
    1/4 sec of sound, and the frames overlap heavily.
  * The waveform is padded with zeros at both ends, to avoid abrupt
    edges on ACF images. In complex sounds, different frequencies
    fade out at different pace, which gives the distinctive shape to
    their ACF images.

Click on the image below to see its high-res version.

[xs]

Below are all the vowel sounds I could find on wikipedia.

[10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [1] [20] [21] [22]
[23] [24] [25] [26] [27] [28] [29] [2] [30] [3] [4] [5] [6] [7] [8]
[9]

10/2022

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