# Signal Processing: Audio Basics

## Keywords

- Harmonic structure of sound
- Parson code of music
- Linear time-invariant theory
- Autocorrelation
- Noise
- Chirps
- DCT compression
- Discrete Fourier transform
- filtering
- convolution

## Linear Time-Invariant System

We describe the system with $Y(t) = f(X(t))$, where $X(t)$ is the input, and $Y(t)$ is the output.

- Linear: $f(a X_1(t) + b X_2(t)) = a f(X_1(t)) + b f(X_2(t))$
- Time-invariant: input $X(t+\Delta t)$ will produce the shifted signal $Y(t+\Delta t)$.

LTI systems are memory systems, casual, real, and stable. Stable means the output won’t reach infinite if the input is finite. It’s bounded.

## Impulse Response

Suppose we have a impulse $X(t) = I(t)$, and output $h(t)$.

Now we have another input $X(t)$, we can ask that what would the output be if we put the input in the same environment as the previous impulse.

$$ \begin{equation} Y(t) = \int d\tau h(\tau) X(t-\tau). \end{equation} $$

## Transfer Function

For the impulse response, the transfer function is obtained through the Laplace transform of the response,

$$ \begin{equation} \tilde h(s) = \mathscr L_s [ h(t) ]. \end{equation} $$

With the response function, we know that the response with some other input that is set in the same environment is

$$ \begin{equation} \tilde Y(s) = \tilde h(s) \tilde X(s). \end{equation} $$

`wiki/algorithms/signal-processing-audio`

Links to:L Ma (2018). 'Signal Processing: Audio Basics', Datumorphism, 03 April. Available at: https://datumorphism.leima.is/wiki/algorithms/signal-processing-audio/.