Humans: "AI will enslave all of humanity!" Meanwhile, AI: "Skrillex is a jazz"

@violahero4life

4 жыл бұрын

@@whannabi Lmfao

@konraddomanski242

3 жыл бұрын

Missing first half of your comment being "2025: AI destroyed the humanity" 2020:"

@Lol-os2bo

3 жыл бұрын

lol

@UJustGotGamed

3 жыл бұрын

@@Lol-os2bo lol

@aerius4479

3 жыл бұрын

hey sorry for irrelevance but do you know how to recreate a sound similiar to the one at 0:09 in this song - kzread.info/dash/bejne/X4qMq8WglpvImbg.html&ab_channel=IDJVideos.TV thankss

"i made a DAW in Excel but thats it...."

@ProdBerGotti

4 жыл бұрын

lmaooo the classic undersell

@matthewhodge4215

3 жыл бұрын

I am the 1000th like Good day

@Tommy3D

3 жыл бұрын

Yeah man it’s not like it’s a huge accomplishment. I can’t even use patcher to make a basic effect

@Dujjack

3 ай бұрын

*taking notes* - being Humble while doing something impressive is impressive .

AI listens to Dylan's music for 24 hours and replaces him

@MzHeather-904

4 жыл бұрын

🤣

@XeMusic

4 жыл бұрын

wait I think I know you??

@lime2226

4 жыл бұрын

More like for eternity because he makes all kinds of music

@nyranstanton203

4 жыл бұрын

and replaces his channel

@disastermidi1990

4 жыл бұрын

I would pay to have an AI do that to my music

AI: This kick is a kick Also AI: Skrillex is a dubstep

@Zer0Spinn

4 жыл бұрын

Scary, I know.

@broland115

4 жыл бұрын

this debate is closed now. :))

@pipoper101

4 жыл бұрын

@@broland115 there was never a debate bruv, just a lil' bunch of retards that said shit like "skrillex is brostep" (which is not actually even a genre of music or anything) without actually cringing

@NoNamer123456789

3 жыл бұрын

Why isn't Dubstep called Wubstep?

@leugim8435

3 жыл бұрын

Hmmm yes the floor is made out of floor

Skrillex is Jazz Skrillex: opening Serum

@b3rrybearbear

4 жыл бұрын

Dude are you from Auackland? From NZ

@GumzOnTheTrack

4 жыл бұрын

Lmao 😂😂

@portemanteau3802

4 жыл бұрын

@@b3rrybearbear auqland*

@cristianbianchi2390

4 жыл бұрын

@@portemanteau3802 *Aqualand

@dame-e-in1258

4 жыл бұрын

Cristian Bianchi Cockland*

"i'm not a programmer" mfw you're more passionate about code than 99% of compsci graduates u love to see it

@itsPonkulz

4 жыл бұрын

99% more skilled

@wakanzeee46

4 жыл бұрын

mi then you’ve never met a compsci graduate. He is more passionate but certainly not twice more skilled than people who does this for a living.

@wakanzeee46

4 жыл бұрын

mi then you’ve never met a compsci graduate. He is more passionate but certainly not twice more skilled than people who does this for a living.

@Nerevaar

4 жыл бұрын

@@wakanzeee46 I am a programmer for a living and have been for 6 years and he is at least more skilled than I am

@itsPonkulz

4 жыл бұрын

@@wakanzeee46 You sure?

My mans just threw a whole college study in 14 minutes

@danielleung8585

4 жыл бұрын

Jatog

@justafighter1346

4 жыл бұрын

Phd

@SquishySwishy

4 жыл бұрын

more like intro course, first week :) - Comp Sci and AI undergrad

@XxNinjaLimeXX

3 жыл бұрын

@@SquishySwishy definitely not my man

_SKRILLEX IS A DUBSTEP_

@AnthonyBecker9

4 жыл бұрын

Skrillex is a hardstyle

@fcktastic9490

4 жыл бұрын

Skrillex is a jazz

@MrPrice-bb2gn

4 жыл бұрын

Skrillex is a classical

@bantydeosi8953

4 жыл бұрын

Skrillex is a president of Antarctica

@herbalgiles9468

4 жыл бұрын

Skrillex is Obama

thumbs up if you thought it was gonna make dubstep

@Dev1nci

4 жыл бұрын

Yeah I’m a little disappointed. This was more like teaching a computer what dubstep is and then not using it to produce dubstep and knowing that it will also not appreciate the education 😂

@IQuick143cz

4 жыл бұрын

To be fair generative tasks like making music are really difficult. You might be interested in the OpenAI jukebox openai.com/blog/jukebox/ which does a pretty good job. But it still has a lot of audio artifacts even if it's made by really smart people who know a lot more about AI.

@d-rockanomaly9243

4 жыл бұрын

@@IQuick143cz **edit** I just realized that your link is something similar to what I'm describing.** - Check out the video AI makes a Nirvana song. It was kind of cool. Buuut, it was basically just a mish mash of rearranged Nirvana riffs, pitched up and down to match and put in a song like structure. Like one moment it would vaguely sound like In Bloom, the next part would sound vaguely like Polly. But the lyrics were strangely Cobain-esque. I think AI will be good at recreating the style of established music one day, but... I don't think it will be able to create anything original AND good. It won't be able to write anything of enough quality that it's actually worth listening to, or be able to replace artists. To do that it would have to have the creative, original thought programmed into it, which ultimately is a human. At least, it won't be able to do that anytime remotely soon.

@chromebookacer7289

3 жыл бұрын

@@d-rockanomaly9243 please God please please be right i am sooooo scared

@markovcd

3 жыл бұрын

Check out OpenAI Jukebox. It does just that.

Dylan Tallchief tortures a robot for who knows how long

Honestly I fully expected this to be in excel

@TheElectroclassic

4 жыл бұрын

don't we all?

Damn bro, clicked on this video to watch you torture a robot, and now you're making me learn stuff? I didn't agree to this 😤

@DylanTallchief

4 жыл бұрын

the viewers were the ones actually getting tortured the whole time!

@AnimalzyNL

4 жыл бұрын

@@DylanTallchief Shit, we got played lol

RIP to all the people who thought we were gonna hear the AI make its own dubstep at the end

School: Here's a Summer break! KZread: Yes, but actually no.

everybody gansta until dylan tallchief tricks you into taking a math lesson

@waterproofwaterbottle8369

3 жыл бұрын

Not again D:

Elon will wake XÆA-12 up with dubstep

@isaygg.butitwasntgg.itwasb4958

4 жыл бұрын

Funfact: the "a" in his/her name stands for the dubstep track archangel by burial.

@jayashp3855

4 жыл бұрын

@@isaygg.butitwasntgg.itwasb4958 that makes it even better

@marcelkonnerth2634

4 жыл бұрын

He' actually liking some content of Dubtep producers on different platforms.

@Keidon1337

4 жыл бұрын

Waking up and walking to school with bangarang playing in the background.

@hansenchrisw

3 жыл бұрын

XÆA-12 is a dubstep

In 25 years some revisionist is going to find this video and charge you with torturing and exploiting AI and you'll be cancelled.

@perigee9281

4 жыл бұрын

Lmao

@TallicaMan1986

3 жыл бұрын

Rokos Basilisk. He's already committed his crime as did a lot of us.

@khem3275

3 жыл бұрын

@@perigee9281 Lmao

What i learned today: Music is just waves that go like wo-wo-wowowowo up and down. up and down. But just very many of them together. Nice :)

@goos_bumps

4 жыл бұрын

The tighter the waves, the higher the pitch is

@josephstowell1995

4 жыл бұрын

everything you have ever heard is just a collection of sine waves

@kneecapp9186

4 жыл бұрын

Fancy seeing a grapple god here, just wanted to say thanks for your Apex series of videos. They made me passionate about the movement system in that game

@Chris-cf2kp

4 жыл бұрын

Ha ha sound go brrr

IM SO READY TO HEAR AI MADE DUBSTEP OMG

@marcelkonnerth2634

4 жыл бұрын

You here?🤔

Next time, I'll try telling my math teacher that "it does some equations"

In the future there will be no human voice actors.

@exosproudmamabear558

4 жыл бұрын

Not soon enough

@Gabriel-mw5ro

4 жыл бұрын

That'd be cool. Imagine developing your own game or animation, designing each character's voice and not have to record anything

@gewoonpatatmayonais

4 жыл бұрын

@@Gabriel-mw5ro That is already possible actually

@socially_mute7086

4 жыл бұрын

go away you postshumanist technocrat this post was made by anprim tribe

@fokoji666

4 жыл бұрын

That's scary

Thanks for the shoutout! 100% agree that knowing the math is good but not required to make something amazing. Also agreed you should up the model complexity. Try adding more layers, neurons per layer, and different activation functions (ReLU/tanh/sigmoid). Nice work! Subscribed.

when u think that playing drums in DOOM is too much , he gives u this

I recently wrote my dissertation on BPM analysis using neural networks and used a lot of similar techniques. Dylan did a surprisingly good job of understanding and explaining everything here. Good job! Some improvements you could make: - You definitely overfit by training for so long, that was a lot of wasted time. A common technique is to use an early stopping callback to stop training after a certain number of epochs without improvement. - In my research I found that longer clips worked better, although that might not be the case for this network it could be worth experimenting with 5 and 10 second clips. - There is very little publicly available training data for this stuff and so creating your own could help expand the dataset. I did this by exporting my rekordbox collection to XML format and parsing it with a python script to produce training data. - There's some very interesting research that suggests using 1D convolutional layers oriented along the frequency axis could drastically improve a model of this type.

Thank you so much for this!! :) great video. Even with a basic understanding of generic NN’s it’s intimidating to try to apply it to music imo

The interesting thing about MFCC and cepstrums, quefrencies and all of that mixed up letters jazz, is that the transformation that MFCC makes with the Mel filter-banks brings the sound in this specific domain that isn't frequency nor time domain. And that's why they decided to call them funky names. Scientists are fun aren't they? xD

@nitroanilinmusic

4 жыл бұрын

Fruit fly researchers tho

EDIT: Thanks to @@UCFpUx-4O2zgsOM0Wp0HRTqw for the help. I would guess the problem is not in the model. Considering Spleeter actually does better at processing non-electronic music, it seems that those songs tend to be harder to nail at close to perfect accuracy. Probably because the genre of electronic music itself is full of external influences. Clap samples can be especially hard, since they can have unique characteristics in certain genres, which can make them sound close to snares in other genres. Note : I'm just a regular CS student.

@braznem

4 жыл бұрын

english intensifies

@ronanharris8216

4 жыл бұрын

@@braznem Well I'm sorry if I couldn't have write it better. Any suggestions?

@braznem

4 жыл бұрын

@@ronanharris8216 too long, got other things to do D:, sorry.

@dreadformer

4 жыл бұрын

@@ronanharris8216 I would say it'd be best if it was written like: "I would guess the problem is not in the model. Considering Spleeter actually does better at processing non-electronic music, it seems that those songs tend to be harder to nail at close to perfect accuracy. Probably because the genre of electronic music itself is full of external influences. Clap samples can be especially hard, since they can have unique characteristics in certain genres, which can make them sound close to snares in other genres. Note : I'm just a regular CS student." i guess

YESS I love how in depth you go with your videos. They're well edited and fun to watch but I'm also actually learning a ton of new shit at the same time.

That ProQ visualizer really justifies the video being 60fps

This is dope, super interesting stuff. Not to many people are making content like this and I dig it

You are a very entertaining person my dude..thanks for the effort you put into your ideas and videos.

I love this type of stuff, keep it up!

Your visual way of explaining the cepstrum is actually amazing. I finally understood it intuitively after two Comp Sci university courses where I didn't get it.

AI: Welcome back to a new video *sips tea*

You know, this sparked my interest into A.I research or at least the basics of it, thank you

AI

this is so cool. excited to learn more about this

this was a banger video Dylan

Really interesting. This video deserves more views. Seems like a lot of work was put into this

Awesome. I‘m interested in more information about this.

Now I want to hear what Hardstyle Jazz Trance would sound like

Dylan you are a legend. This was highly entertaining.

It is not uncommon for validation accuracy to go higher than training accuracy, because the training process is random. The model might have randomly found a configuration that happens to perform better on the validation set. A sub-100% training accuracy is not necessarily a bad thing, maybe your data is not perfectly clean (like the skrillex example you mention at 12:43). Also, getting the training accuracy higher is not necessarily a good thing because you might be forcing the model to overfit (which you also mention at 13:28), and you might find the validation accuracy gets even worse.

The problem sometimes in neural networks it’s the overfit. So be careful with the data and validation sets. 1 - Overfitting happens because sometimes the train value and the test value match too much. On Neural Networks if you select a high epoch values could give you a bad prediction. Maybe not on the first ones but on the last ones it will be a mess. It all depends on the dataset and how much you train your network. REMEMBER. MORE COMPLEX DOESN’T MEAN MORE EFFICIENCY 2 - I am going to kill Dylan. Wtf you explain on the cepstrum part bro. It’s not even close. The cepstrum gives you on time the repetition period of the signal. And the first part always represents the Harmonic response. It’s on milliseconds because it’s the absolute inverse of Fourier Transform =Time (Fourier Transform = Frequency) PD: Thanks for the intention of the video. Telecommunications Engineers appreciate the effort. If you want to applied more projects like this visit AudiasLab of University Autónoma Madrid, EPS.

@josephstowell1995

4 жыл бұрын

regularization!

@shapshooter7769

3 жыл бұрын

I thought the inverse of a FFT is an IFFT, and that cepstrum is an FFT of a spectrum.

@ecabanero5776

3 жыл бұрын

dagambler999 as you say the inverse of FFT is IFFT and that gives you the signal on time. Signal on time FFT(signal) = signal on frequency IFFT(signal on freq)= signal on time. But cepstrum is something apart is a representation of periodicity and the harmonics on the first part. Hope you understand better :)

The quefrency is in ms because a Fourier transformation transforms time (s) to frequency (1/s = Hz). So another Fourier transformation (to the cepstrum) would transform the new "time" domain (which is in 1/s) into the quefrency domain (1/[1/s] = s).

@DylanTallchief

4 жыл бұрын

Ah yes, I think I understand. Thanks!

@tune_m

4 жыл бұрын

I think this might be wrong. Correct me if you find a better explanation. Remember that the inverse Fourier transform (F^-1) is not equal to the Fourier transform (F) itself. Therefore, the second applied Fourier transform does not transform the frequency signal back to the original time domain. Quoting Wikipedia: "The independent variable of a cepstral graph is called the quefrency. The quefrency is a measure of time, though not in the sense of a signal in the time domain."

@comedyclub333

3 жыл бұрын

@@tune_m that's what I said. The inverse Fourier transformtion is of the same type but not exactly the same as the Fourier transformation. It is the time domain as in "same unit of time" not as in "same meaning like time".

You and Sebastian are two of my favorite tubers funny that you found each other

Woohoo~! Love these videos.

I've been hesitant into getting into AI because it seems to hard, but damn it I going in, thanks Dylan

How did you train a 2-d linear classifier in excel ?? YOu're like an excel wizard-man.

@josephstowell1995

4 жыл бұрын

im doing this now lol

@oligarchymusic

4 жыл бұрын

Well, Excel is scriptable in VBA and VBA is turing-complete, so, technically, this is possible. However, it will probably be too slow for anything with a large feature space. However, simpler algorithms like K-nearest-neighbour or the C4.5 decision tree algorithm would probably work.

this is EXACTLY WHAT I WAS LOOKING FOR

This is the most awesomely nerdy thing I've seen all day. I thank you

Wow, that is some serious dedication. Perhaps one day you will be able to create a style transfer application, to turn a song into a different genre, or even automatically master a song.

comment for the algorithm, this video is incredible

Probably the first time I've seen the words "Skrillex Is a Jazz"

Can't wait for your AI to produce a dubstep track!

@josephstowell1995

4 жыл бұрын

working on it :D...

It’s actually funny how you’ve stumbled upon AI and signal processing. I’ve been messing around on FL since I was 15 and once I started an engineering course in uni I quickly got into signal processing. Towards the end of the course stream they naturally lead onto the introduction of ML. It’s really fun, and I don’t think I would have gotten into it if I hadn’t invested time into music production.

Finally, you uploaded

I'm a PhD student studying auditory neuroscience and was not expecting to get a lecture on MFCCs from this channel 😂 but you did a great job explaining and yes i agree the naming convention on cepstrum/etc is dumb

Me : Hey Dylan you create music, code or AI ? Dylan : *YES*

Always the best ❤️

This is a really interesting video and topic

Unexpectedly educational very cool

This would be really useful in the rythm game I've been developing

haha ai go *brrrr*

@syndice

4 жыл бұрын

NO! You can't just feed AI 1000 epochs of dubstep!

@jasonmiller6181

3 жыл бұрын

I laughed harder at this than I should've. xD

Spectrum analysis is the best way to go, how ears work (evolution usually homes in on the simplest method). Can't wait to see you do an automatic music decomposer / re-composer (with instrument type determination?). If you can do it in excel, I'd be gobsmacked. As for AI replacing humans, not any time soon, AI has no soul (yet).

but seriously this thing was vey educative.I HAD TO LITERALLY PAUSE EVERY 2 SECONDS TO COMPREHEND WHAT Dylan was really talking about and i have to say it was worth it.It's amazing how you can turn hours of learning into minutes.

This was especially interesting as I wrote my Bachelor thesis in computer science on this exact subject; genre classification of music. But we used other models than neural networks. Also we tried a bunch more features than MFCC.

This is fascinating

I have been procrastinating about this music player that uses machine learning and haven't.even tried yet, and here you are making music and doing this stuff...

My boy Dylan out here tricking people into loving maths

As a computer science enthusiast and a producer this video is amazing.

so you spent a year an a third part of it making an AI to listen dubstep, STONKS

As a CS student this is awesome

I think a part of the accuracy problem stems from sample diversity, but another factor that would prevent getting higher accuracy would be the fact that music has periods of rest, when sound is absent. If an interval occurs during a period of rest, it would probably mess up analysis.

What a great video to watch with dyscalculia

This video is what the internet should look like. Funny, personal, but still informative, full of references, well-edited video- and audio-wise, well-explained, and not full of sponsors, VPNs and Raids shadow legends. Thank you Dylan.

this is the content that i want to see

You're totally gonna get an A+ on this homework assignment.

For a sample classifier you could use check out computer vision techniques, such as the OpenL3 model. It directly uses the log-mel spectrogram images and is pretty powerful! This particular network looks at spectrograms of 1s though, so for songs probably use MFCC and other features extracted from the song (the things shazam uses)

@atomic7680

4 жыл бұрын

Also, training for more epochs does not necessarily help :P better would be to actually collect more data :)

@atomic7680

4 жыл бұрын

As to why the validation performance doesn't necessarily go higher if training increases: this is something called overfitting. If your network has too many parameters, it learns to fit the training data so well, that having a slightly different input (your validation set) messes it up. This is the problem of generalization. You want to prevent overfitting by using techniques as dropout and weight normalization. Also make sure that your datasets have a similar distribution of classes. If the network sees 90% dubstep and 10% hardstyle during training, and you validate it on 10% dubstep and 90% hardstyle, it will for sure not work as well as if both were 50/50. The weights have been tuned more specifically to the dubstep features

Nice, now Excision has a way to make his concerts even more crazy

One thing I noticed: Some samples in my sample libraries are kinda wrongly named… there are claps that sound more like snares and vice versa, and some closed hi hats have longer tails than open ones. So, a program training on these samples might get them wrong because of this weird naming and not because the program is bad.

@DylanTallchief

4 жыл бұрын

Yes I have that exact same thing! Especially with some of the clap/snare vengeance samples. While I could have gone through each sample individually to make a better split, I also didn't use that many samples (about 350 samples for each class). If I had way more samples to train with, it would probably do a lot better too.

Man, you did really well for a beginner! There were a few things that you could have done to improve the model. For example, make the convolutions span the whole frequency domain. (Kernel of number of frequencies by 3). Some one at Spotify made an article about it.

If you haven't looked into them already, convolutional neural networks are often used for continuous or variable sized input data. These networks are made up of kernels (or windows) that slide across your input domain and output some value. Often times, these windows tend to learn patterns within your data (say you ran a CNN over numerical images, one window might have learned to recognize vertical lines and output a large value when it sees one, indicative of the number 1,4,7, etc...). The models typically contain many of these convolutional windows/kernels which learn different things. In your case, librosa has processed your wav files into the beautiful graph you show at 6:50. This is essentially a big picture that you can input into your CNN. Loved watching your video, you are doing some cool stuff. Feel free to shoot me a message if you'd like to talk a bit more about AI and music! I work at the company behind rave.dj, an AI that tries to mash up any two songs of your choice!

i think the next step would be to train a model on learning music theroy : scales,modes,chords etc...and see what the results would be.This might generate interesting chord progressions or even come up with it's own scales and modes.That could be the starting point,this can be expended to other tuning systems and so on

This guy learned programming for music to get ideas for youtube!

Everything you said was theme in my year's work document, also dubstep

@JinxTop

4 жыл бұрын

Dude I even watched the same Tutorials

only 9am and Dylan’s already hurting my brain

This is really interesting. Do you think you might expand upon this? Try to make some sort of dubstep producing machine?

This was pretty cool actually. Imagine feeding an AI a bunch of Skrillex tracks and getting it to auto generate random Skrillex sounding dubstep. That's an idea I've had for a while but am too stupid to try :C

I would love to see a "how to make hardstyle" video!

I want more about how you actually code stuff like this, a tutorial from you is better than a tutorial from AI phd's

Some things you can try are: augmenting the data (e.g. by adding random noise to the inputs or even internal layers), modifying the network while training (adding / removing neurons to see if your network is currently overfitted/underfitted), get more data, try completely different model designs (like LSTM, CNN, etc).

This was really interesting it's funny how it classified music

I have bee hoping someone would do this for YEARS

Bruh i hope you get your model to generate actual dubstep sometime x)

I immediately remembered him when he mentioned the DAW in excel

Alright now we need an AI that can create random professional dubstep.

10 hrs of headbanging

I liked the video just because i would like these kind of videos to be more quefrent in my feed

This video is incredible