Aliasing... Or How Sampling Distorts Signals
Audience: undergraduategraduate
Aliasing is one of those concepts that shows up everywhere - from audio and imaging to radar and communications - but it’s often misunderstood or oversimplified. In this video, we break down exactly what aliasing is, why it happens when sampling signals, and how it leads to distorted or misleading results if you’re not careful. We’ll start with the core idea of how sampling works, why the Nyquist limit exists, and what it actually means to sample “too slowly.” Then we’ll walk through intuitive visualizations and concrete examples to show how high-frequency signals can appear to be completely different - sometimes even disappearing altogether. Whether you're working in DSP, RF, or just curious about signal processing, this video gives you a foundation to understand aliasing and how to avoid it. There’s also a link in the description to a companion Python notebook where you can explore the concepts interactively.
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Comments
Scores good on motivation and clarity/
I loved the animation style - the dynamic elements give a very clear visual intuition for how the aliasing effects manifest in the time vs frequency domains. For an undergrad/graduate audience, maybe some more concrete equations would have been appropriate to help motivate these ideas in context, but overall the explanation is clear and concise.
Superb! This is professional-made.
I thought this was an excellent video. The speed of topic was great, it was digestible and had more information if I wanted to learn more. This is exactly the format I like to consume.
Very nice animations, and a topic well worth covering. I would’ve appreciate more details about how convolving with a dirac comb in the frequency domain is that same as in the time domain.
It may have helped to spend more time showing the parallels between the things happening in the frequency domain and the equivalent processes in the time domain. I say this because I find it difficult to develop intuition for something being presenting in the frequency domain, so it helps if its shown in both domains.
illustrating the sine signals
I love the idea of having a python/jupyter notebook for the extra ability to dig in for interested viewers.
The visuals are polished and the motion is smooth and guides the eye. The use cases are valuable at the beggining, but student’s should be reminded of the consequences of the theorem with a real life example. For example, what if we sample at less than double the frequency rate. What consequences would that have for patients who are having medical imaging done.
Really impressive animation.
Great video! I like the visualization and description of how the calculated peaks are moving when the sampling frequency is changed.
I enjoyed the video and think that it is well scoped, i.e. it describes the topic quite comprehensively without trying to fit in more than it needs.
I think the jump into the frequency domain could have been handled a bit more cautiously. While it was fluent and well executed, some words to ease in people who haven’t been there for a while could have been helpful.