Скачать или смотреть ESP32 Spectrogram Scrolling FFT Data time-based frequency visualization.

Spectrogram (Scrolling FFT Data)
Time-based frequency visualization.
Similar to how a spectrogram in music software looks.

Search for this library: KissFFT by Mark Borgerding
Step 1: Capturing Audio Data
The ESP32 ADC (Analog-to-Digital Converter) reads the sound sensor's analog signal and stores it in an array. This signal represents the air pressure variations (sound waves) in digital form.

Step 2: Fast Fourier Transform (FFT)
To analyze sound frequencies, we use FFT (Fast Fourier Transform), which converts the time-domain audio signal into a frequency-domain representation. This helps us understand which frequencies are present in the sound.
Instead of arduinoFFT, we use kissFFT for better performance:

Step 3: Creating the Spectrogram (Scrolling Effect)
Once we have the frequency data, we display it on the OLED screen by scrolling the spectrogram left and adding new FFT data on the right.
How it Scrolls:
1.Move previous spectrogram data left by one pixel.
2.Map FFT bins to OLED height (lower frequencies at the bottom, higher at the top).
3.Normalize intensity to control brightness.
4.Draw the new frequency data as vertical lines on the rightmost column.

Step 4: Making Vertical Lines Thicker
By default, the spectrogram draws single-pixel lines. To make vertical lines thicker, we draw multiple pixels for each frequency bin, making the bars more visible:

Summary of How It Works
1.Sound sensor → ESP32 ADC reads the analog sound signal.
2.FFT (kissFFT) converts the sound into frequency components.
3.Spectrogram scrolling effect moves old data left and adds new frequencies on the right.
4.OLED draws frequency bars with intensity based on signal strength.
5.Thicker lines improve visibility, making the spectrogram more readable.
Code:
https://github.com/ukkokalevala/Spect...