Come On Barbie, Let’s Go Party: Using AI for Music Mixing

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As a DJ, I use many techniques to mix and create new sounds that get peoples’ hands in the air. In this talk I’ll describe AI algorithms based on Neural Networks which have the ability to break down music into elements. I’ll cover how our brain differentiate between dozens of different sound signals when we listen to music. Can we instruct AI to do so? 

The cool part: live DJing on stage using AI algorithms.

This talk has been presented at JSNation 2024, check out the latest edition of this JavaScript Conference.

FAQ

DJ mixing is the art of blending two or more tracks seamlessly to create a continuous flow of music. It involves curating the right playlist, reading the crowd, and determining the next track based on the energy on the dance floor.

Deep learning revolutionizes DJ mixing by allowing DJs to separate and manipulate different components of a track, such as vocals and instruments, in real-time. This technology uses neural networks to analyze and process sound signals, enabling more creative and precise mixing.

A data scientist at Wix builds machine learning pipelines for data scientists across the organization. They work on extracting and processing data to improve various aspects of Wix's services, including website building and user experience.

The DJ in the text mixes Dark 80s, synthwave, and techno sounds.

Common visual representations of sound include waveforms and spectrograms. Waveforms show the amplitude changes over time, while spectrograms display the changes in frequencies over time, providing a 3D representation of sound.

The three main levels of audio feature abstraction are: 1) Low-level features, such as amplitude, envelopes, and zero crossing rate, which are numerical data for machines to process. 2) Medium-level features, such as pitch, beats, and notes, which are more perceptual for humans. 3) High-level features, such as tempo, lyrics, melody, and rhythm, which are enjoyed by general listeners.

Deep learning helps in music source separation by using neural networks to process raw audio or spectrograms and extract different components, such as vocals and instruments. Convolutional neural networks (CNNs) identify local patterns, while recurrent neural networks (RNNs) find dependencies between track parts. The result is a mask that separates the desired audio component from the original track.

Applications of music source separation technology include karaoke, music transcription, lyrics extraction, and beat matching. This technology allows for high-quality separation of vocals and instruments, making it easier to create karaoke tracks, transcribe music, and enhance DJ mixes.

The DJ was inspired to explore music source separation using neural networks after receiving an email about a new technology in his DJ software that could separate track sources. Initially dismissive, he later revisited the feature to help a friend separate vocals from an old track, leading to his amazement at the technology's capabilities.

The DJ used open-source tools, datasets, and Python code to experiment with music source separation. He followed step-by-step guides, read articles, and trained models himself to understand and utilize the technology.

Ziv Levy
Ziv Levy
27 min
13 Jun, 2024

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Video Summary and Transcription

Today, we explore DJ mixing and how deep learning revolutionizes the art by discussing sound processing, extracting features, and using machine learning. Deep learning allows for efficient extraction of audio features and high-resolution track separation. Neural networks can achieve source separation by converting audio to spectrograms and applying convolutional and recurrent neural networks. This has immediate impact on industries such as karaoke and music transcription.

1. Introduction to DJ Mixing and Deep Learning

Short description:

Today, we're going to explore DJ mixing and how deep learning revolutionizes the art. I'm a DJ at Wix and a data scientist. DJing is more than curating playlists, it's about reading the crowd. Sometimes, when I try to blend a song that sounds perfect in my headphones, it crashes on the dance floor. Let me show you an example. We'll discuss sound processing, extracting features, and using machine learning. And then, we'll dive into the revolutionary deep learning approach.

Today, we're going to actually explore and dig into this art of mixing, of DJ mixing, and I'm going to talk about this perspective as a DJ and actually we're going to also talk about how deep learning brings a whole new revolution to this music art of mixing and in general what can be done with sound signals and neural networks.

So, again, a bit more about myself. I'm working at Wix for the past seven years now. I'm working at the data science group. My day job is that actually I'm building machine learning pipelines for data scientists across the organization. For those of you who are not familiar with Wix, Wix is a website platform building. And again, I'm also a DJ. I'm mixing Dark 80s, synthwave, and techno sounds and this is what we're going to talk about today, again, this aspect of my life as a DJ.

And I don't need to tell you that being a DJ is not only curate the right playlist, but it's also the ability to read the crowd and to see what track is going to be next according to the energy on the dance floor. And the issue is, like, the problem is that sometimes I hear something very good that really perfectly fits to the dance floor in my headphones and when I try to blend it in to the dance floor, it crashes. Let me show you how I am crashing a mix. And how awful this sounds. So I picked those two songs. One of them is by Adele. You are familiar with this song, right? And the next one is, oh, not this one. Next one is this one. Also familiar. By the way, everything I do, I'm doing it live. So if I have some glitches or some messed up, just excuse me. Okay?

So in my head, those songs are perfectly matching. But if I'm trying to play it, and let's skip to this, to the highlight of the Adele song. I'll try to mix the song exactly at the highest point of it. Okay. As you have heard, it's a lot of noise. This is where, you know, some of you probably would do some faces of, hmm, what? What's wrong with this DJ? But to my fortunate, you will be surprised to see what, you know, a very drunk crowd may overcome. But for me, it's like it's devastating. It's really like ruining the moment, and the energy is unbalanced, and I need to recover from it, and it's very stressing. But again, in my head, it was perfect. So what was it? So what we're going to talk about today is what sound is and how we process audio with computers and how we're pulling out features from this audio, and how we use it in machine learning. Okay? And then we're going to talk about the deep learning approach, which is pretty much revolutionary.

2. Exploring Source Separation and Sound Modeling

Short description:

It all started with an email about a unique technology for separating track sources. I didn't pay much attention until a friend asked for help in separating vocals. I rediscovered the tool in my DJ software and was amazed by its real-time capabilities. Intrigued, I delved into music source operation using neural networks. Sampling measures amplitude levels, resulting in a waveform that holds information about frequency, intensity, and timbre. Computers struggle to distinguish between instrument overtones, unlike our brains.

And as we speak, things are really happening right now. So it all started with, you know, back a couple of years ago, I got an email from the release note from the DJ software that I'm using, and they're saying something like, here, dear DJs, we are now able to provide you a unique technology that will allow you to separate the sources of your track, and by that, you know, be creative and do something with it. And I thought to myself at first that, well, it's not so interesting. I mean, probably it has been solved already. But you know, it was like post-Covid era, there were still limitations, and you know, limitation on crowding and everything, so I really didn't pay attention to that.

And recently, a friend of mine came to me and she said, I want your help to separate the vocals out of some track that I have. This is a very old track, there are no studio versions or something. What can I do? And you know, sometimes I have my equalizer here, and I can play and, you know, in some manner reduce the sound of some sounds, or enhance the sounds of others, but it's not really creating a karaoke version of, like, peeling apart the layers. But suddenly I remembered that I have this tool in my DJ software, and I read, you know, the step-by-step guide of what to do, what do I need to configure, click a few buttons, and boom, I had it. And I was, you know, it was nice, she was happy, but then I, like, played with it with another song and another song, and I was actually, it wasn't just nice. I was amazed by that, and everything was happening in real time.

And this is something that was not on this release note, by the way, but actually, or maybe it is, but I didn't read the entire thing. But actually I was amazed, so this really triggered my engineering part of the brain. And, you know, I started, what do I do? I want to know how things are happening, I go to Google. And I look for music source operation using neural network, and I downloaded an article, read it, another article, read it, downloaded the dataset, downloaded the Python code, trained the model myself, and then I was testing it with another track and another track and another track, and I was actually mind-blowing about this technology. And after a few hours of playing with it, this is how I look like. Like, I discovered, you know, a whole new world came out to me.

So, the first thing is how we model sound, okay? What sound is? So, sound, eventually, is like changing in air pressure caused by air molecules' vibrations. Our ears are sensitive to those vibrations, and eventually this is what our brain perceives as sound. So computers does something similar called sampling. I'm not going to dig into this technique because of time constraints, but the computer measures the amplitude levels of those vibrations. Eventually, what we get is a waveform, which is the most common visual representation of sound, but actually this waveform holds multifactorial information about the sound. The first thing is the frequency, okay? If we zoom in, we can get the frequency of the sound. Second thing is the intensity of the sound. The intensity is measured by a squared, like we are taking a squared area of the waveform, and we see what is the peak in proportion to what is the minimum and the maximum points. And then we have something very important, which is the timbre of the sound. And the timbre is something that also considered as the tone quality or the tone color. It's not the quality like how clear I hear the sound, it's the tone quality of like overtone of different instruments overtone each other. For example, if I'm playing a C chord at the same time I'm playing a C chord in a guitar, at the same time someone plays a C chord at the piano, I want to be able to distinguish between those instruments, and this is something very hard to do for computers. Actually, if you think about it, our brain can do it pretty much instantly.

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