Smaart v7 - Understanding The FFT

[@Mikee535]

I think the FFPO - floating point /octave measurement found in Smaart 5 was way better for my purposes. How can I emulate that in Smaart 7? What does MTW mean?

[@dudemcgee256]

I now have a better understanding of FFT after watching this video. I don't think the others were listening....lol

[@xMasterxRazorx]

I love how Jamie's job description changes with each title.

[@poonamrana5550]

xMasterxRazorx

[@laughingbonsai]

After reading your replies to other videos, it seems that YOUR COMMENTS are a waste of time... This video describes the FFT in context to Smaart.

[@3dModels4Sale]

thanks

[@Misterlegoboy]

and you're the assistant line cook? how?

[@kdanladi]

Thx, man

[@j7ndominica051]

I find it non-intuitive that an analyzer of this type can extract information about bass energy at all with FFT sizes shorter than 1 cycle at that frequency. I can accept that it is so (with say 512 and 576 points commonly used in audio codecs), but it is bizzare that even a precise DC value can be retrieved from that data. If I look at a waveform, I want to see a repetition before I can think of frequency. Also how is it that analyzers with good detail in the low end only appear in modern times (combining different FFT window lengths in one display, very slow), while equalizers have always existed capable of precisely differentiating say 55 and 60 hertz, a natural logarithmic freq resolution? But that precision was never available in visual analysis. I don't know anything about the underlying maths...

[@laughingbonsai]

LOL I guess we have to rename this video, so many people don't understand that this has to do with the FFT in context to Smaart. Understanding the mathematical equation provide almost no benefit to sound engineers, but understanding why you should use different sized FFTs is useful. BTW Jamie is not a line cook, it's a joke dude, you're obviously not from around here

[@christsanjoures]

these comments are so funny -

[@johnywhy4679]

Cool vid, but it does not explain FFT at all. It explains the logarithmic nature of human hearing, which is also a very important concept that non-musicians/non-audio mathematicians don't always realize. Your graphs would be significantly more understandable if there wasn't such a massive amount of noise in your signal. At first, it does not seem intuitive at all that the graph is a representation of your voice because it's so swamped with noise. thx

[@DTA707]

WTF does this have to do with FloridaFitnessTwins?

[@jameshopkins3541]

Howw??????

[@JonathanCruz-0_o]

Dude you do realize this video is 5 mins long and doesnt explain anything about the Fast Fourier Transform ... as there is never any mentioning about the mathematics behind it ... And that is why cooks should cook, not talking about things they actually dont understand.

[@jameshopkins3541]

ME NO UNDERSTANDING

[@waterkeeper03]

like i'm gonna take audio advice from a line cook...

[@GizmoMaltese]

This tells the viewer nothing about FFT much less "Understanding the FFT" so I gave you a thumbs down.