This is the first time I read such an excellent explanation. It instantly made me understand the weighting and working principle of Digital Filter. Thank you so much.
What an absolutely fantastic explanation video with just the right amount of depth but also clarifying some basics aspects. Why cant professors teach like this..
I really needed this explanation! I am taking an image analysis course and the Instructor talked about the application of low pass filter for smoothing an image but I could not grasp the underlying idea. Thanks to you for this awesome video!
I used similar functions for my application but didn't know there is some mathematical approach for this called "High pass and Low pass filters"..... Thank you so much for explaining.
Wow you are an absolutely Amazing teacher i have SEVERE A.D.D and find it very difficult to give anyone or anything my full and undivided attention especially when trying to pay attention to someone giving a lesson on something. even on a subject im really interested in my brain usually trails off n i have never been able to figure out why my brain does it or how i can stop it from doing so and thanks to you i believe I have possibly figured out one of the causes for it I noticed that you had my full attention until you used a word that i know but never really knew its meaning and just as my mind started trailing off to ponder the words meaning and usage you explained the words meaning and usage n pulled my attention back to the subject at hand which your explanation was Amazing by the way i started watching this video to better understand high and lowpass filtering in music production you just made me look at it from a totally different perspective and its application to sound design in a completely different way thank you for that
Good, very good. The 1D filters, otherwise known as moving average, is routinely used in denoising the environmental data such as rainfall, temp,... The 2D extension, these filters are widely used in raster analysis & remote sensing of environment, crops,forestry, and so on. I would appreciate more of this. Thank you.
Teaching is not just science and knowledge but art and you are a real artist. Can I use some of your explanation in my dissertation and reference your youtube videos or any other reference you prefer please? thanks.
They don't call it a kernel in signal processing, they call it a window function. Statistics calls them "kernels". Interestingly, the use of window functions predates eg. Holt-Winters Triple Exponential Smoothing by over a hundred years and statisticians failed to give credit appropriately.
Nope. In DSP the Kernel is the array you convolve by. A window is different from a kernel, when you window a signal you are multiplying it point-by-point with a shaping function. Kernels are about neighborhood of operation. There is a slight difference.
@@MIkeGazzarusoFunny, Prabhu uses window function in his book on filtering. I stand corrected, thanks! So kernel = when convolved, window function = multiplied but not summed.
Great video, thanks a lot! I am wondering: How would I provide the cutoff frequency (when applying this filter to some audio signal) to a filter implemented that way?
Thank you very much for your explanaition. There is one question that came to my mind though when applying a LP on a time series, it woud be like taking a moving average. But where is the lag? If the time series were values I get from a sensor, using a LP, would give me the filtered values, but lagging by half the length of the width of my array. Or am I missing something here?
In the video, on the time series graph the high freq is shown in the middle, in signal processing the Low pass content of the signal is typically around zero and high freq is away from the zero? the high freq content here seems more like a "Bandpass signal"
Dude thank you so much. My teacher gave us these horrible UA-cam videos to watch. Also is it just me or do most circuits videos and books suck? I find great chemistry and calculus videos and stuff like thermo and mechanical stuff, but I feel like there is a lack of good electrical information out there. Also I struggle with circuits more than anything else so maybe that's why I feel that way.
![Bandpass & Bandstop Filters: Simplest Digital Design Ever? [AudioFX #011]](/img/n.gif)
You are the Khan Academy of UA-cam. Having an above average communication skill. Keep it up dude!
Dude, really well explained. You're a great teacher! Just earned a sub!
Agreed! +1 from me too
So much lack of clarity explaining these concepts elsewhere, thank you for being CLEAR!
Man i was just studying this and then you dropped this vid! earned me instant clarity. thank you
Glad to hear it!
This is the first time I read such an excellent explanation.
It instantly made me understand the weighting and working principle of Digital Filter. Thank you so much.
What an absolutely fantastic explanation video with just the right amount of depth but also clarifying some basics aspects. Why cant professors teach like this..
i swear Ritvik, I learn more from you then from my graduate program. Great work!
Happy to hear that!
One of the best explanations I have ever had about low-pass, high-pass filters. Thank you so much!
Glad it was helpful!
Fantastic explanation! I first studied this stuff 20+ years ago and needed to do some refreshing, and your video really helped.
Excellent! This was a nice introduction, thank you very much!
I really needed this explanation! I am taking an image analysis course and the Instructor talked about the application of low pass filter for smoothing an image but I could not grasp the underlying idea. Thanks to you for this awesome video!
Found this while looking for help with an assignment for my radiology schooling. Thanks for helping me better understand these!
Thank you so much for these videos. Please keep doing this!
More to come!
well explained. there are so many professors that just cause uncertainty at stuff like this, which actually is quite easy to understand
I would say most professors at universities just simply aren't that good at teaching.
I used similar functions for my application but didn't know there is some mathematical approach for this called "High pass and Low pass filters".....
Thank you so much for explaining.
This is actually EXACTLY what I needed.
This channel is a god send, Thanks.
Glad you enjoy it!
Amazingly well explained. You save my master thesis ! thanks a lot !!!
Wow you are an absolutely Amazing teacher i have SEVERE A.D.D and find it very difficult to give anyone or anything my full and undivided attention especially when trying to pay attention to someone giving a lesson on something. even on a subject im really interested in my brain usually trails off n i have never been able to figure out why my brain does it or how i can stop it from doing so and thanks to you i believe I have possibly figured out one of the causes for it
I noticed that you had my full attention until you used a word that i know but never really knew its meaning and just as my mind started trailing off to ponder the words meaning and usage you explained the words meaning and usage n pulled my attention back to the subject at hand which your explanation was Amazing by the way i started watching this video to better understand high and lowpass filtering in music production you just made me look at it from a totally different perspective and its application to sound design in a completely different way thank you for that
Simple and concise explanation. Great video.
The best intuitive explanation, gives me a deep understanding, Thanks.
Good, very good. The 1D filters, otherwise known as moving average, is routinely used in denoising the environmental data such as rainfall, temp,... The 2D extension, these filters are widely used in raster analysis & remote sensing of environment, crops,forestry, and so on. I would appreciate more of this. Thank you.
Yes, definitely!
Thank you. This helped me understand signal processing of electrodermal activity.
Glad it helped!
Really great video, dense, concise, well presented :) Thank you!
Cheers - I really enjoyed your explanation!
Ritvik, this video is fantastic! Thank you for the help
Glad it was helpful!
Outstanding.
incredible video!
Great and brief explanation.👍
This is really well explained and I was able to understand it well enough to use it in my project immediately. Thank you!
Great to hear!
Really great video - thanks!
Glad you liked it!
great video, many thanks
Great class, thhanks!
This is really helpful, thank you!
wtf this is the greatest explanation of all time.
Thanks for your videos, they are very clear and useful. Please consider also to make videos on Kalman, Hamilton, and particle filtering.
I saw yesterday for the first time your channel and videos and I'm shocked with your amazing work! Could you make please a video for EGARCH?
really cool !!
Thank you!
Thank you 🙏
Awesome!
Wow!, thank you
Soo good! Thx a lot!!
thanks i magicaly finaly understand it
thank you kindly
Thank you
BEST💥💥💥💥
Thanks a lott❤
thank you
Thanks!
very helpful!
Very nice explanation!!!!!!!
Glad you liked it!
Very informative!
Glad you think so!
Teaching is not just science and knowledge but art and you are a real artist. Can I use some of your explanation in my dissertation and reference your youtube videos or any other reference you prefer please? thanks.
Thanks
讲的真几把太好了！受教了！蟹蟹1
Thank you great lecture. Do you have additional videos for electronics?
Excellent explanation, thank you! Question: What then is a band pass filter?
great video🙌 hey can you provide any guidance about the concept of the Hodrick Prescott filter?
They don't call it a kernel in signal processing, they call it a window function. Statistics calls them "kernels". Interestingly, the use of window functions predates eg. Holt-Winters Triple Exponential Smoothing by over a hundred years and statisticians failed to give credit appropriately.
Nope. In DSP the Kernel is the array you convolve by. A window is different from a kernel, when you window a signal you are multiplying it point-by-point with a shaping function. Kernels are about neighborhood of operation. There is a slight difference.
@@MIkeGazzarusoFunny, Prabhu uses window function in his book on filtering. I stand corrected, thanks! So kernel = when convolved, window function = multiplied but not summed.
Great video, thanks a lot! I am wondering: How would I provide the cutoff frequency (when applying this filter to some audio signal) to a filter implemented that way?
Thank you very much for your explanaition.
There is one question that came to my mind though when applying a LP on a time series, it woud be like taking a moving average. But where is the lag?
If the time series were values I get from a sensor, using a LP, would give me the filtered values, but lagging by half the length of the width of my array. Or am I missing something here?
rockstar!
In the video, on the time series graph the high freq is shown in the middle, in signal processing the Low pass content of the signal is typically around zero and high freq is away from the zero? the high freq content here seems more like a "Bandpass signal"
Please make videos on panel data regression....please
thanks so much for dumbing it down for me lolololol
what an entrance 🤣
Would filtering all inputs before function be same or different, than applying filter to function output?
So differencing to make a time series stationary is simply applying a high-pass filter?
And it also shifts the phase? What effect it have on data?
Dude thank you so much. My teacher gave us these horrible UA-cam videos to watch. Also is it just me or do most circuits videos and books suck? I find great chemistry and calculus videos and stuff like thermo and mechanical stuff, but I feel like there is a lack of good electrical information out there. Also I struggle with circuits more than anything else so maybe that's why I feel that way.
Can you also please do video on fast Fourier transform?
I'll look into it, thanks!
Hi, Ritvik I'm little confused, how come filtering related to frequency, when it is making operation on amplitudes? Thank you
Go Buins!
How can we reconstruct a filtered signal to the original signal if we know the filter used?
Good question! This technique is called deconvolution and I'm planning to make some videos on convolution and deconvolution soon
Can you talk about the kalman filter?
Great suggestion!
What did you actually studied?
make a patreon bro
luk in ur face I am guess u'r a professional diver