One representation of a sound wave is a sequence of amplitudes, expressed as binary values. Each sequential chunk of N bits is a number, and that number represents the amplitude of the sound signal at a moment in time. Those moments in time are spaced at equal intervals. One common sampling rate is 44.1 kHz.
That number is chosen because of the nyquist-shannon sampling rate theorem, in combination with the fact that humans tend to be able to hear sounds up to 20 kHz.
The sampling rate theorem says that if you want to reproduce a signal containing information at up to X frequency, you need to sample it at 2X frequency.
To learn more about this topic, look for texts, classes, or videos on “signal processing”. It’s often taught in classes that also cover electronic circuits.
That’s pretty dense reading, but if you’re willing to stop and learn any math you encounter while reading it, it will probably blow your mind into a whole new level of understanding the world.
I honestly wish I had gotten into all the science and physics of signal processing, taken calculus etc, I feel like I’ll pick up a lot of the more qualitative stuff over time particularly if I’m able to apply it in building certain apps that do some novel manipulations and obviously some of that will require me to get an operational understanding of how to put all these blocks together.
One representation of a sound wave is a sequence of amplitudes, expressed as binary values. Each sequential chunk of N bits is a number, and that number represents the amplitude of the sound signal at a moment in time. Those moments in time are spaced at equal intervals. One common sampling rate is 44.1 kHz.
That number is chosen because of the nyquist-shannon sampling rate theorem, in combination with the fact that humans tend to be able to hear sounds up to 20 kHz.
The sampling rate theorem says that if you want to reproduce a signal containing information at up to X frequency, you need to sample it at 2X frequency.
To learn more about this topic, look for texts, classes, or videos on “signal processing”. It’s often taught in classes that also cover electronic circuits.
Here is an example of such a text
That’s pretty dense reading, but if you’re willing to stop and learn any math you encounter while reading it, it will probably blow your mind into a whole new level of understanding the world.
I honestly wish I had gotten into all the science and physics of signal processing, taken calculus etc, I feel like I’ll pick up a lot of the more qualitative stuff over time particularly if I’m able to apply it in building certain apps that do some novel manipulations and obviously some of that will require me to get an operational understanding of how to put all these blocks together.