Amazing video! One little comment: 2 to the power of 1/12 is not equal to 1.059. Instead it is very close. I think this is kind of important to mention because 2 to the power of 1/12 is in fact irrational and we often want simple rationals because they generate a more pleasing sound. Therefore, the piano is often not equally tuned and we have different "gaps" between two notes.
Hi Velardo, I will be soon watching all your videos because I am trying to make a very cool project. Don't stop creating such great educational videos! Looking forward for more!
This series is awesome so far! I have liked and subscribed. I'm currently in college as a math major, interested in doing ML and data science. In high school, I was very interested in music, and even did my senior project around math and music. I have not thought about that project in years, but this video is bringing back all my feelings of excitement surrounding the math of music. Can't wait to learn more!
It would have been perfect if A4 was at key 69 and 420 hz. Petition to use 420hz system! Great videos btw, really helps me a lot to understand things about sound that i'm missing coming to audio from NLP!
Incredible video as always Valerio, can't wait to see more. I've been experimenting with taking clean audio and augmenting high/low/band pass filters as well as white noise/ external audio file noise using signal-to-noise ratios in Python. My end goal is to use some of these processes to transform clean data into public communications - quality radio data for purpose of training an RNN-LSTM model. I have had some interesting results so far and I owe it to these videos for getting my thoughts in order as someone with little signal processing background. Cheers!
A very well researched video with clear explanation. Ironically, the only improvement would be to adjust the volume of the video. So up with the amplitudes 😎
I really like the way of explanation about each basics of audio. This series is really help me to learn about audio processing. Thank you very much for your this series. Will catch in slack or linkedin to clarify my doubts. I wish you all the success. keep up good work .
21:06 It's a little unclear, but I think that's supposed to be (2^((p-69)/12))*440. In other words (since I feel like my version of the equation might be a bit hard to read), (p-69)/12 is an exponent.
This was incredibly explained!! Thanks dude :) I just subscribed looking forward to learning from you, i've been thinking about how to learn programming from a music perspective, I just hit the jackpot with your channel. I wish you all the best
Very good explanation, could you maybe make a video about Fourie Transforamtion and DFT, with a bit of mathematical background...anyway, keep up the good work :)
You have said that an orchestra is an example of periodic complex type of waveforms. As far as I know the same goes for human speech. But how is it possible? I don't really get the idea of how is it periodic. I see that sine wave is periodic, but how an orchestra or human speech can be? Is it because the sound made by orchestra is combined by sine waves? By the way, great videos. I am a student of acoustics engineering and this helps A LOT, and explains things easily. Thank you so much for that.
You got it right. Speech / the sound of an orchestra can be thought of as the combination of many sinusoids with a certain arrangement of peaks (harmonics). This will become clearer later in the series, when I'll discuss the Fourier transform in detail.
First of all, wanted to say amazing video without a doubt. Helped me a lot in getting started in the audio domain. Just wanted to ask a dumb question based on the slide at 10:54 and 11:30? Based on the equation you provided of the sin wave, is the relation of the frequency and amplitude always inverse? Are there any exceptions?
4:18 why is sound shown up and down from zero (horizontal axis) when both (up and down) are identical? Would it not save half of memory to only show/capture half of the part (perhaps only the positive part?)
Hi, can you please make a tutorial for Audio Data Augmentation (Noise Addition, Time Stretch, Pitch Shift, Resample, Time Mask, Frequency Mask, Overlay Augmentation...etc)
It is really a great series on audio. Very useful. I had one doubt/observation. In the waveform diagram, the diagram looks very symmetric. But why is it so? Shouldn't sound from a random source be very erratic? But in the diagram, the pressure = 0 line seems like a mirror. So is it really the case or am I mistaken somewhere?
The quality of your contents is exceptional! You sir deserve a million subscribers, Keep it up!
Thank you Sang!
This series just saved me from getting fired. I owe you big time!!
I am sharing this youtube channel with my students. This is gold.
Thank you very much Nitin!
Amazing video!
One little comment: 2 to the power of 1/12 is not equal to 1.059. Instead it is very close. I think this is kind of important to mention because 2 to the power of 1/12 is in fact irrational and we often want simple rationals because they generate a more pleasing sound. Therefore, the piano is often not equally tuned and we have different "gaps" between two notes.
Good point!
Incredibly well explained and very high production value. Your enthusiasm for the topic is contagious :D
Thanks!
Hi Velardo, I will be soon watching all your videos because I am trying to make a very cool project. Don't stop creating such great educational videos! Looking forward for more!
This series is awesome so far! I have liked and subscribed. I'm currently in college as a math major, interested in doing ML and data science. In high school, I was very interested in music, and even did my senior project around math and music. I have not thought about that project in years, but this video is bringing back all my feelings of excitement surrounding the math of music. Can't wait to learn more!
It would have been perfect if A4 was at key 69 and 420 hz. Petition to use 420hz system!
Great videos btw, really helps me a lot to understand things about sound that i'm missing coming to audio from NLP!
you made me understand, think and see waves from a different perspective. Thankyou so much
Incredible video as always Valerio, can't wait to see more. I've been experimenting with taking clean audio and augmenting high/low/band pass filters as well as white noise/ external audio file noise using signal-to-noise ratios in Python. My end goal is to use some of these processes to transform clean data into public communications - quality radio data for purpose of training an RNN-LSTM model. I have had some interesting results so far and I owe it to these videos for getting my thoughts in order as someone with little signal processing background. Cheers!
Thanks a lot! It's a pleasure I can be of help :)
Beautiful. I hadn't understood this from my seventh grade physics class until now.
Wonderful series for any beginners who want to understand sound processing, thank you!
great video. One improvement suggestion would have been to include some audio samples but other than that truly fantastic
Thank you for sharing your knowledge and expertise field. You have kind heart and sweet man.
your series is exactly what i was looking for, thanks for this content sir
These are awesome, thanks so much for making this playlist!
You're the best, thanks so much for these free lectures :)
Thank you very much. This taught me more than several videos on YT.
Just started watching the series. Amazing content
A very well researched video with clear explanation. Ironically, the only improvement would be to adjust the volume of the video. So up with the amplitudes 😎
My friend, you are the best! This is amazing content!
I really like the way of explanation about each basics of audio. This series is really help me to learn about audio processing. Thank you very much for your this series. Will catch in slack or linkedin to clarify my doubts. I wish you all the success. keep up good work .
Thank you!
I am so glad I found this channel, Thank you so much.
This is what i have been looking for , thank you sir you did a great job indeed ❤
thank u sir for ur information .im so exsatide to complete this series of this vidios
This is astounding content quality!
Thanks!
Thank you for your video. Very good explanation.
Valerio, thank you. These videos are GREAT.
Thanks!
you make things very easy to understand, greaaaaaaat thanks
dude thanks so much! i wanted to learn more on the first principles of sound. this is exactly what i need.
Fantastic :)
awesome summary Thanks Valerio you are my hero !
Nice explanation and informative content as usual, Valerio
21:06 It's a little unclear, but I think that's supposed to be (2^((p-69)/12))*440. In other words (since I feel like my version of the equation might be a bit hard to read), (p-69)/12 is an exponent.
saved me some time tnx!
@@chainbreaker8909 : No problem, glad my comment was useful.
Amazing video content !! Very well explained Can you also post some videos on using wavelet transform for audio feature extraction?
I haven't planned tackling wavelets any time soon.
Appreciate your hardwork beautiful videos
Thanks!
Very well done! 🌊
It's really informative
Great Lecture!
Thank you!!
This was incredibly explained!! Thanks dude :) I just subscribed looking forward to learning from you, i've been thinking about how to learn programming from a music perspective, I just hit the jackpot with your channel. I wish you all the best
Thanks a lot Donny :)
Very good explanation, could you maybe make a video about Fourie Transforamtion and DFT, with a bit of mathematical background...anyway, keep up the good work :)
I think I will -- stay tuned :)
Very nice explained. And I am not an English Native.
Thanks!
Whatever you mentioned as frequency is known as wavelength!!
Again, Thnxxx, so much ! 🙏🙏🙏
Thanks for your great efforts... could you please differentiate between higher perception and louder
You have said that an orchestra is an example of periodic complex type of waveforms. As far as I know the same goes for human speech. But how is it possible? I don't really get the idea of how is it periodic. I see that sine wave is periodic, but how an orchestra or human speech can be? Is it because the sound made by orchestra is combined by sine waves?
By the way, great videos. I am a student of acoustics engineering and this helps A LOT, and explains things easily. Thank you so much for that.
You got it right. Speech / the sound of an orchestra can be thought of as the combination of many sinusoids with a certain arrangement of peaks (harmonics). This will become clearer later in the series, when I'll discuss the Fourier transform in detail.
Thank you so much. I am going to watch and probably will be asking more questions :D thanks a lot again!
Like for Queen reference XD, and tutorials is very easy to follow. Great work!
Thank you!
Hallo. Thanks for jour job. Very intresting Thema. Can you Explane now Detect the Notes with Librosa?
First of all, wanted to say amazing video without a doubt. Helped me a lot in getting started in the audio domain. Just wanted to ask a dumb question based on the slide at 10:54 and 11:30? Based on the equation you provided of the sin wave, is the relation of the frequency and amplitude always inverse? Are there any exceptions?
Great video!! It would be greatly appreciated if you could share the reference (books, papers, articles etc) that were used for this video
Thanks! For these initial videos, I'm loosely following Fundamentals of Music Processing (www.springer.com/gp/book/9783319219448).
4:18 why is sound shown up and down from zero (horizontal axis) when both (up and down) are identical? Would it not save half of memory to only show/capture half of the part (perhaps only the positive part?)
After you shows the piano keyboard, I did not know what you are talking about.
Hi, can you please make a tutorial for Audio Data Augmentation (Noise Addition, Time Stretch, Pitch Shift, Resample, Time Mask, Frequency Mask, Overlay Augmentation...etc)
Great !
With this course and your other course "Deep learning for audio", am I able to developpe an "speakers recognition into a wave file" app ?
thanks
Thanks for these videos, you are a crack! (as we would say in Spanish ;) )
Thank you Matias :)
Higher the sound VS louder the sound. Can you explain the difference with an example.
Are these equations for equal temperament, just intonation, or something else?
Based
It is really a great series on audio. Very useful.
I had one doubt/observation. In the waveform diagram, the diagram looks very symmetric. But why is it so?
Shouldn't sound from a random source be very erratic? But in the diagram, the pressure = 0 line seems like a mirror.
So is it really the case or am I mistaken somewhere?
Thank you! Can you point to a timestamp?
@@ValerioVelardoTheSoundofAI yes at 3:29, the waveform graph.
@@sandipdutta4354 this is a random waveform I plotted from a piece of music. These waveforms generally have a loose horizontal simmetry.
@@ValerioVelardoTheSoundofAI Thanks you. Understood.
P(1,000,000 subscribers | this informative content ) = 1
Thanks :)
I could not join the slack channel. Am I doing something wrong?
Same here!
For music noobs, 17:33 he said 'semitones'. I looked for 'symptoms' and 'sentence' until I found the word.
Thank you for pointing that out! I should have put the word "semitone" somewhere on a slide.
Very informative - but WAY too many ads. Had to give up.
10:25
9:29
Your sound is way too low. Whenever an ad plays I'm getting earraped.
I had this issue with old videos. With the latest ones I fixed the problem.