A signal is the data that is transferred from one system to another. If you wave a hand to your friend on the other side of the street, that's a signal. If you set up an antenna, you hunt for (TV or radio) signals, and so on. They can be mechanical, visual, digital, electrical, etc.
In our case we will talk about analogue audio signals. They start in various ways (a digital read, a mechanical vibration, magnetic data, …) to be later converted into electric signals, which are later transferred into mechanical ones and later into pressure (speakers).
We have already seen what a sine signal looks like in the first article, but let's see some more of it.
The sine signal is the primary signal and it is the most natural way of an oscillating behavior. In the physical world, a simple pendulum behaves as a sine.
We may say that a sine is the primary signal because every periodic function/signal can be decomposed into a series of sines through a mathematical tool called Fourier series.
The sine signal is defined as y(t)=A*sin(ωt), where y(t) is an amplitude position in time t, A is a scalar (the extreme value of amplitude), ω is the angular frequency of the signal multiplied by full round in radians (2Πf), and t is the time in a specific moment.
The sine wave is a signal that is mostly used to test the response of audio systems.
To test the speed of an audio system, we use the square signal. This is the type of the signal that has only two values: half the period is on the extreme positive amplitude value, half the period is on the extreme negative amplitude value.
y(0..Π)=A ; y(Π..2Π)=-A
With this kind of a signal we can test how a system reacts to extreme speed situations. The more it is similar to the input signal, the better it is. On the same graph, two typical misbehaviors of the system are illustrated: overreactive (red) and slow (blue). The latter is a very common reaction of the passive components like capacitors and coils.
Sometimes there are also other types of signals used for testing, like the triangular wave and the saw.
In all the examples above, we have seen periodical signals, signals with uniform and repetitive shape in time related graphs. It is easy to follow the system with these kinds of signals. In the real world, while listening to music (or movie, or speech, …), the signal usually looks like that:
Pretty messy and impossible to get a real picture of what's going on. In this case we prefer analyzing the data with frequency dependent graphs. More of it in the next article.