Hello world it's Siraj and have you ever wanted to create songs using AI it's totally possible
And I've got my friend Taryn here to help me explain. How it all works. Thank you for having me time
I've been watching your videos for a while and very excited about this moment in time
Thank you. Hey everybody
I'm Taryn southern and I've been a youtuber for quite a long time
Got my start making music videos on YouTube about 10 years ago, and recently released my first single off of my upcoming album
Which is composed entirely with artificial intelligence as Suraj said so?
Really been exciting to work with this new medium
Totally, it's super cool. You guys got to check it out links are gonna be in the description
Okay, so why did you want to create songs using AI I?
Think it it seemed like a great challenge. You know I just I like doing things that I haven't done before
Music creation has always been a little bit of a mystery to me because I don't play instruments I grew up
Trying to learn how to play guitar
But my hands were so small and not very nimble across the guitar that I basically gave up
I played a little bit of piano
But I had this narcoleptic piano teacher who would fall asleep halfway through the lesson so that didn't work out so well either so
for me
Writing music in my in my 20s in adulthood
Really just became about writing by ear and so when I found
The first pieces a few pieces of software that could actually
write music using code
I got really excited because I thought now finally someone like me that doesn't play instruments could potentially make the music that I hear in
my head with this new collaboration partner, so it was an exciting challenge and
It's like crossing new frontier. Yeah, I mean who does that?
I mean and this new partner being AI yep, and they never get tired or hungry
I
Don't need to have Doritos
Anyway, and if you don't like their song you just throw it out you throw it out the window. It's gone. It's gone
It's out of here
Okay, so so tell us about the process you used to do this what yeah
So it's different depending on the software and there are now a number of different AI software companies out there
that can compose music from Google magenta to ampere to AI music I've
IBM Watson
Like I'm forgetting some of the main ones. I'm collaborating so the all function differently depending on the neural network and how the
how the engineer has actually created the interface so I will say that the
Easiest one to use if you just want to get started and understand what kinds of things say I can actually create musically damper
And that's the one that I used for the single that I released in September called break free the cool thing about ampere is it
actually produces all of the music so
What I mean by that is all the instrumentation that you hear in that song is actually
Put together using AI it wasn't it was not a musical track composed by AI performed by humans
So it gives you a sense of what's possible because it's actually really hard to take
Random musical sounds and put them together into something cohesive. That sounds good
So I would start with ampere. That's that's my cut Tiggy. That's a Suraj's backpack oh
Don't do that
She's destroying your backpack I'll just have an AI make one with generative adversarial networks right okay perfect perfect
So if you're looking to do the most basic
kind of
introductory
Lesson in AI music creation I would go to ampere if you are familiar with code
And you want to get your hands a little bit dirty
I would say Google magenta or IBM Watson are great places to go google magenta their software
And I have a number of different programs available for AI music writing, but two different things
It's all available on github
I believe I think and IBM Watson is actually releasing their software open source in January
And I've been using that the last month to write stuff, and I love it. It's cool. So you do need to have knowledge
Coding knowledge you'll be using terminal to write most of your songs
Yeah, I had to like for the first couple weeks on it. I was calling their developers being like wait
How do I do this thing so basically with a program like Watson or magenta?
You're going to set different musical parameters like what type of song you want
The AI to create is it pop is it reggae is it a mixture of reggae and pop what's the tempo?
What's the you know? What's the BPM? What are the instruments that you want embedded into the track?
And in some cases you can also give the AI a track or a multitude of tracks to
Teach it
Basic rules based on those tracks that you like so if you want to make a song that sounds like the Beatles
You basically just feed it a whole catalog of Beatles songs
It'll do its thing, and then you say I want the song that it creates to be a reggae track
So now you've got a reggae track inspired by the Beatles
Right so, so there are a lot of depending on how skilled you are at coding
There are a lot of ways that you can take
These these software's to do really cool things
There's a program in Google called incent
Which allows you to actually take two disparate sounds and combine them together
So I could actually record my cat Iggy scratching on Suraj's backpack and then combine that with the sound of nails on a chalkboard
to create a truly delightful
He decided to say hi
This is the little runt that's ruining the video what I can't even fake you. I don't really like cat stuff
But essentially I mean
You even you can create a whole an entirely new sound using some of these tools and inject that into your musical work, so
This all goes to say that they function differently
Depending on what you choose to use and your coding knowledge and your musical knowledge
But it is absolutely untrue that using AI is not a creative process
It's so creative because there are so many different avenues that you can take in creating a song and creating new sounds
And I actually think that it has the ability to bring out the super composer in all of us
totally awesome
So how does amber make this all happen well it's proprietary technology, so we're out of luck. See you next time
Just kidding we can totally reverse-engineer how it's very likely done looking at some of the latest research in deep learning
we know that one popular way to class machine learning algorithms is in two categories of generative and
discriminative models discriminative models classify data in two sets of categories
They allow us to differentiate between different types of data
generative models on the other hand offer even more
exciting possibilities creating new data after going through a training phase one
Type of generative model are called the audio synthesis models these are useful across a range of applications that we use day-to-day
including text speech systems the kind that Google Translate uses to help dictate what you've just
Translated or the kind that voice assistants use to respond to our requests generic synthesizers have been used in music
Extensively and have a long history in that field for DJ's - terrible hip-hop producers like t-pain
They're usually hand designed in-house that gets
Acceptable signals like pitch and velocity to shake the tone and dynamic of a sound
Synthesizers have had a huge impact on culture and music
The past few decades as some of the most popular sounds out there have used them
So the traditional way of doing this will so use some sort of arrangement of oscillators or an algorithm for sampling playback
But a newer way to do
This is by using a data-driven approach that is letting an algorithm learn from musical data
it's possible to create new types of expressive and
realistic instrumental sounds using deep learning
We know that deep learning allows us to learn features from data
And in the case of music these features can represent tone
timbre dynamics Krunk ness all of these can be tunable knobs for music generator
Google, has been at the forefront of a lot of progress in AI generated music and they have an open source project call magenta
Magenta embodies a lot of the recent models that have been published in that field
A more recent model just published a few months ago used two concepts together
The first was a wavenet style auto encoder that learns temporal hidden nodes to capture longer-term
Structure exactly and the second was called incent which is an enormous data
Set of music that can be used to explore neural audio synthesis of musical notes. Just like I mentioned earlier so incense contains about
300,000 four-second annotated notes
From about a thousand musical instruments an order of a magnitude more than any other similar data set so it's pretty powerful
I'm like I said you can combine sounds of your cat with I don't know anything anything which is pretty cool
Deep neck criticism call wavenet
it generated raw audio waveforms and Weidman models the conditional probability of
two of a waveform to generate new music
It uses all previous samples and other parameters to do this the authors of our audio synthesis paper were inspired by
That model to create something similar to weight net
There were two motivations here the first was to be able to create music that had a consistent long-term
Structure so that the theme would carry out through the end of the piece unlike anything off of eases
The second was to use the learned features for applications by using meaningful audio
Interpolations like between different instruments for example like what what turned it in the original wavenet
architecture and each step of training a stack of dilated convolutions
Predicted the next sample of audio from a fixed size input a prior sample values
the joint probability of some audio was factorized as a product of
Conditional probabilities between the different ways, but these researchers remove the need for external conditioning instead
It works as an autoencoder a generic one taking in raw audio waveform as input from which the encoder produces an embedded
Then they shifted the same input and fed it back to the D color which reproduce the wait input waveform
So using the joint probability they could parameter eyes a feature as a latent variable to train the model
They use gradient descent the most popular optimization scheme out there shoutout to grade a descent
And it uses the difference between the predicted output
And the actual output as an error value then using that error it calculates a gradient and using that gradient
It subsequently updates the weight values of the network over time to give it better predictions
It was trained over eighteen hundred thousand iterations a really long time for the training data even though ensign
annotated every single note using its unique pitch and timbre and
dynamics they decide to further annotate it giving each sound a source a
Family of instruments and a sonic quality even though training this type of deep learning model requires a massive amount of computing power
You could just download a model from github and run it on google cloud or AWS
this stuff is
Democratized so you have access to computing power if you use the cloud or you could just pull model off of the magenta github page
Or use experiments google.com and play with it in your browser so three things to remember here
Generative machine learning models that can learn latent variables from a data set can be used to generate
new data similar to the training set
You can use a wavenet style auto encoder and the end synth data set if you'd like to experiment with state-of-the-art
machine learning from music generation and
Wavenet is perhaps the most powerful model out there to perform audio generation you have a really smart audience
All about them
They're very smart if you want to see more AI music you can follow me at youtube.com slash Teheran there
We go please subscribe for more programming videos, and I think we gotta go we gotta go make some AI music
That's true, so thanks for watching