What are features in signal processing using a more tangible or less abstract description I'm learning about signal processing for music, and in my reading I keep finding the term &quot;features&quot;. I find that they never really give a concrete example of what they mean by &quot;features&quot;. They'll say stuff like features related to timbre, but they won't really go into further depth than that. I also took an image processing class once and found that I didn't really understand it there either. I understand what some literal features of a sound would be, like say distortion, but I have a feeling that this isn't really what they're talking about when they say features.

Example
I'm reading something on Dynamic Time Warping(an algorithm that seems similar to local/global alignment) and they say this

To compare two different features x, y ∈ F, one needs a local cost
measure, sometimes also referred to as a local distance measure, which
is defined to be a function c :F×F → R.

My understanding of Dynamic Time Warping is that two similar waves(∿), aligned to each other by measuring the distance between each subsection of one wave to each subsection of the other wave and then choosing the alignment that minimizes the total distance between all the wave sections. I'm doing this for soundwaves, so why are they describing it as comparing two different &quot;features&quot;?

Obviously it can't be described in a way that is 100% tangible, but I'm looking for a description of features that one would be able to draw out or make a picture of.
A feature is a number that describes one aspect of a signal.
Signals can be very complex, and the simplest analysis tools (like a time plot, a spectrum, or an energy measurement) don't tell you everything; in fact, for specific types of analyses, they almost don't tell you anything useful.
So, features are designed to describe very specific aspects of a signal.
To make an analogy, let's say you're an expert in ice cream and want to compare two brands. Just looking at a picture or the list of ingredients is not enough. You can taste them, but you can't accurately describe the taste to your colleagues (just like you can listen to sound but you can't describe exactly what it sounds like).
So, you come up with &quot;features&quot; about the ice cream: the density, the statistics of the nitrogen bubbles inside it, the sugar/fat ratio, the crystalinity, whatever. Some of the features will be useless, but some will turn out to be really good at objectively conveying something interesting about ice cream.