Speaker 1: Technology with tech Stuff from stuff works dot com. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm a senior writer with how stuff works dot com.
Speaker 1: I talk about all things tech and today we're gonna
Speaker 1: get a little musical with things and get a little
Speaker 1: help from our buddy Noel. Noel, who is the producer extraordinary.
Speaker 1: He's the head of of of podcast production here at
Speaker 1: how stuff Works, also one of the co hosts of
Speaker 1: Stuff they Don't Want You to Know. Noel went to
Speaker 1: mog Fest in and and got the chance to talk
Speaker 1: to a whole bunch of really cool people, including Alexander
Speaker 1: Lurch and we'll hear more about that a little bit
Speaker 1: later in this podcast. Mog Fest ostensibly is about music
Speaker 1: and technology, but it actually involves lot lots of other
Speaker 1: stuff to not just not just those two already broad fields,
Speaker 1: but other ones as well, including elements of philosophy and
Speaker 1: and even particle physics. Will have an episode in the
Speaker 1: near future that will include some elements from uh interviews
Speaker 1: we had with folks from the Large Hadron Collider. So
Speaker 1: mog Fest has all sorts of really smart, talented people
Speaker 1: getting together and having these incredible symposia and and and performances,
Speaker 1: And so Noel was able to go and talk with
Speaker 1: someone about some really cool stuff, and that kind of
Speaker 1: ties into what I wanted to chat about today. You know,
Speaker 1: once upon a time here at How Stuff Works, we
Speaker 1: had a show called Stuff from the B Side, and
Speaker 1: this was a podcast all about music. Episodes focused on
Speaker 1: everything musical, including elements that are more general concepts or
Speaker 1: philosophical ideas. And music and technology are two things that
Speaker 1: really do closely tied together. After all, almost every musical
Speaker 1: instrument is some form of technology, ranging from the relatively
Speaker 1: primitive versions of certain percussive instruments all the way up
Speaker 1: to high tech digital rigs. So I thought it might
Speaker 1: be cool to revisit music and tech and look at
Speaker 1: a particular subset of it, musical analysis and music generation. Now,
Speaker 1: music analysis and technology are also related in that we
Speaker 1: now have various automated recommendation engines that will suggest music
Speaker 1: for us to listen to based upon what we've already
Speaker 1: said we enjoy. Now these engines look for new pieces
Speaker 1: of music that in some way match criteria we seem
Speaker 1: to find appealing. We have indicated to that service that
Speaker 1: we like that particular type of music, so it starts
Speaker 1: to try and find matches that kind of follow in
Speaker 1: the same lines. As they become more adept at figuring
Speaker 1: out what qualities we really enjoy, they can hone in
Speaker 1: on songs that appeal to us, perhaps even changing them
Speaker 1: up based upon other criterias, which is the time of
Speaker 1: day or an activity. We're doing so, for example, with
Speaker 1: Google Music, and this show is not sponsored by Google
Speaker 1: Music or anything of that nature, but it will detect
Speaker 1: if I'm on my way somewhere. It might suggest music
Speaker 1: that would be conducive to a trip, or if it
Speaker 1: knows that I'm at the gym, it may suggest music
Speaker 1: that's good for keeping my heart rate up, stuff like that.
Speaker 1: So we'll just imagine a hypothetical situation. I've just woken
Speaker 1: up and the recommendation engine might find some peppy music
Speaker 1: to get me on my way. So Google Music is saying, hey,
Speaker 1: it's Monday morning, you need all the help you can get.
Speaker 1: Here's a radio station based off the song Walking on
Speaker 1: Sunshine by Katrina and the Waves. And then my phone
Speaker 1: detects that I'm going to the gem, so then the
Speaker 1: music engine switches to the song's meant to keep me
Speaker 1: moving at a particular pace while I desperately try to
Speaker 1: find the exit to the gym. I'm sorry, I'm uh
Speaker 1: to actually work out. So in that case, it's probably
Speaker 1: you know, something with a nice driving beat a good
Speaker 1: tempo to it. These are basic things that music engines
Speaker 1: can do now, but the reason they can do them
Speaker 1: at all is because of music analysis. This isn't always
Speaker 1: done in an automated fashion. In fact, automating music analysis
Speaker 1: is pretty tricky. Sometimes it relies instead on just a
Speaker 1: lot of work, and that's work done by real, live,
Speaker 1: human beings. So let's take the Music Genome Project for example.
Speaker 1: This is the database that the internet radio service Pandora
Speaker 1: relies upon when it creates a radio station based off
Speaker 1: an artist or a song that you've submitted as the
Speaker 1: seed for a new channel. For more than ten years,
Speaker 1: Pandora's staff have analyzed and categorized music, breaking down songs
Speaker 1: into all the basic components, which they call genes. These
Speaker 1: are the elements that make songs what they are. And
Speaker 1: I find this approach both fascinating and and a little odd,
Speaker 1: because in a way, it seems a little weird to
Speaker 1: take a really awesome song. Let's say it's um Blue
Speaker 1: Oyster Cults, Don't Fear the Reaper, one of the best
Speaker 1: songs ever written. And then you have to sift it
Speaker 1: down to all those little basic components, those genes that
Speaker 1: make up that song. It also reinforces this notion that
Speaker 1: a song is more than just the sum of all
Speaker 1: its parts. If you were to look at those components
Speaker 1: and attempt to make a song that included all of them,
Speaker 1: I bet it wouldn't be half as awesome as Don't
Speaker 1: Fear the Reaper. So you take a song, you identify
Speaker 1: all these different qualities of it, and may involve things
Speaker 1: like the tempo of the song, the the the structure
Speaker 1: of it, as far as versus and choruses are concerned,
Speaker 1: the whether what kind of vocalists there are, what kind
Speaker 1: of instruments are used, all of these different individual, tiny
Speaker 1: components of the song, and you put them into say spreadsheet,
Speaker 1: and that represents the collection of genes that are possessed
Speaker 1: by Don't Fear the Reaper. You take that same collection,
Speaker 1: you give them to a musician and say, I want
Speaker 1: you to write me a song that has all of
Speaker 1: these components in it. Well, again, probably not gonna get
Speaker 1: Don't Fear the Reaper. You'll get something, and maybe it
Speaker 1: will be good. Maybe it'll even be better than Don't
Speaker 1: Fear the Reaper. I doubt it, but yeah, there's there's
Speaker 1: something magical or apparently magical about music that transcends the
Speaker 1: quantitative elements that we can list now. Pandora's Music Genome
Speaker 1: project identifies four hundred fifty different musical attributes or genes.
Speaker 1: They include lots of different types of data. Some of
Speaker 1: them are relatively straightforward, such as does the song have
Speaker 1: a vocalist? If it does have a vocalist, is it
Speaker 1: a male vocalist or a female vocalist? Are there multiple vocalists?
Speaker 1: Then starts getting way more granular. So if a song
Speaker 1: has electric guitar, for example, there might be a subset
Speaker 1: of information about that, such as how much distortion is
Speaker 1: on that guitar? Does it have a lot of distortion
Speaker 1: in this song or not a lot? And so you
Speaker 1: start to subdivide down the line. Same thing is true
Speaker 1: for other instruments as well. Now, not all songs have
Speaker 1: the same number of genes, meaning some genres of music
Speaker 1: are actually easier to describe with a fewer terms than others.
Speaker 1: For example, rock songs have about one fifty genes. You
Speaker 1: can break down your rock song into about a hundred
Speaker 1: fifty different little individual components. Rap songs are more like
Speaker 1: three d fifty. So that indicates that there are gradations
Speaker 1: and variations between different songs within the same genre. Uh So,
Speaker 1: to make a recommendation engine, you first have to put
Speaker 1: all the music within the library. Through this process, you
Speaker 1: need to identify the important qualities that make the music
Speaker 1: what it is is. And you could use something like
Speaker 1: a spreadsheet and you lay it all out, and then
Speaker 1: when someone wants to make a new radio station off
Speaker 1: of a song, you can use that song's genome all
Speaker 1: the jenes listed for that specific song to guide a
Speaker 1: decision engine to pick other songs that are similar to
Speaker 1: the first one within a certain degree. So you could
Speaker 1: set this dynamically in your search engine. Right Like, let's
Speaker 1: say that you are the one designing the new, latest
Speaker 1: and greatest version of Pandora, and you've got this enormous
Speaker 1: database of music that's all been analyzed by professionals. We're
Speaker 1: talking about actual musicians and musicologists who have listened to
Speaker 1: the music, broken it down into its basic elements identified
Speaker 1: all of them, and someone has joined your service and
Speaker 1: they say, I'm going to make a radio station based
Speaker 1: off the song, Uh, the statue got me high by
Speaker 1: they might be giants. You would end up accessing the database,
Speaker 1: pulling the record for the statue that got me high,
Speaker 1: looking at all the genes that are associated with that,
Speaker 1: and then you would look for a certain percentage of
Speaker 1: similarity with other songs, like are there other songs that
Speaker 1: have the same genes as this song does? If so,
Speaker 1: serve it up see if the person likes it. You
Speaker 1: might set the threshold higher or lower. If it's a
Speaker 1: song that's particularly avant garde. There may not be a
Speaker 1: lot of other songs that strongly resemble your original, so
Speaker 1: you have to kind of play fast and loose with this. Now,
Speaker 1: an important component of this service is user feedback. Services
Speaker 1: like Pandora nearly always include a method for users to
Speaker 1: indicate if they like or don't like a particular song.
Speaker 1: The recommendation engine uses that data to fine tune its selections.
Speaker 1: No two songs are going to be exactly alike, so
Speaker 1: it may be that the ways the news song deviated
Speaker 1: from your seed songs format were the parts that made
Speaker 1: you detest it, So it could have been that the
Speaker 1: the the engine said, well, this song resembles the seed song,
Speaker 1: the original tune of the way. Let's serve it up
Speaker 1: and you listen to it for like three seconds, you say, no,
Speaker 1: this is this is not what I want. You give
Speaker 1: it a thumbs down. The algorithm might say, all right, well,
Speaker 1: I'm gonna keep note of where it was the same
Speaker 1: and where it was different from that original song. Meanwhile,
Speaker 1: I'll serve up this next song that has similarity. And
Speaker 1: if you say, yeah, that's a good song. I really
Speaker 1: like it, and you give it the thumbs up, then
Speaker 1: the recommendation engine starts looking at the differences between the
Speaker 1: song you said no two and the song you said
Speaker 1: yeah too, and it starts to identify stuff that you
Speaker 1: might not even be aware you don't like. It might
Speaker 1: be certain elements of songs, and the recommendation engine has
Speaker 1: figured it out. Maybe it's figured out, oh uh, Jonathan
Speaker 1: really doesn't like it when there's a clarinet in the
Speaker 1: song for no reason, but he isn't able to vocalize
Speaker 1: that he doesn't he's not aware of it consciously, but
Speaker 1: every time it's popping up he's saying no to that song,
Speaker 1: So we're gonna We're gonna put the kai bosh on
Speaker 1: the clarinet from here on out. That was just a
Speaker 1: random example. I don't I don't have a hatred of
Speaker 1: the clarinet, but it is a way for the engine
Speaker 1: to work with the user in order to get a
Speaker 1: better understanding of the type of songs that it should
Speaker 1: serve up to you. Now, there are plenty of other
Speaker 1: ways to analyze and describe music besides this genetic approach.
Speaker 1: There are entire courses dedicated to this. Musicology is a
Speaker 1: rich and interesting field, and some of these approaches go
Speaker 1: beyond the components that are directly perceptible. These analytic methods
Speaker 1: try to capture the essence of the feel of music.
Speaker 1: For example, if you take a bunch of components individually,
Speaker 1: you might quantitatively describe the music with accuracy, but you
Speaker 1: can't capture how they collectively create a particular effect. Perceptual
Speaker 1: analysis attempts to bring human perception and emotional reaction into
Speaker 1: account with everything else. But why is the Music Genome
Speaker 1: project powered by humans? Why is Pandora using actual human
Speaker 1: beings to listen to music and then write out all
Speaker 1: these genes, couldn't you find some easier way? Well? Listening
Speaker 1: to music and being able to describe its structure beyond
Speaker 1: some relatively simple angles is a particularly tricky computational problem.
Speaker 1: It's something that's easy for humans and hard for machines.
Speaker 1: In two thousand five, Way Chai of m I T
Speaker 1: wrote a paper titled Automated Analysis of Musical Structure in
Speaker 1: which she laid out the challenges of creating an automatic
Speaker 1: approach to analyzing music. Her pay Earth is nineties six
Speaker 1: pages long, and that kind of gives you an idea
Speaker 1: of how complicated a problem this is that we're talking
Speaker 1: about here. China's team relied on music cognition, machine learning,
Speaker 1: and signal processing to segment and analyze pieces of music,
Speaker 1: with the goal of isolating and analyzing the recurrent structures
Speaker 1: of a piece. You know, the whole verse, course, verse,
Speaker 1: all my fellow Pixies fans out there, the chord progression
Speaker 1: or key changes that are present in music. Identifying parts
Speaker 1: of a piece that make it representative of the whole.
Speaker 1: In other words, finding that hook or finding that element
Speaker 1: of a song that make it stand out. China's team
Speaker 1: had to figure out how to make a machine do
Speaker 1: stuff that we tend to do naturally, even without the
Speaker 1: benefit of formal musical training. So, for example, I have
Speaker 1: never taken any class beyond music appreciation, which is about
Speaker 1: as one oh one as you get, and yet I
Speaker 1: am able to voke realize certain things about music easily.
Speaker 1: I can recognize these differences, things that a computer cannot
Speaker 1: natively do without all and it requires a whole lot
Speaker 1: of work. The whole paper is available to read online.
Speaker 1: It's really interesting. I recommend checking it out. There's a
Speaker 1: PDF you can just download for free and read over it,
Speaker 1: and it's fascinating. It delves into not just the programming
Speaker 1: challenge of creating this analysis software, but also the peculiarities
Speaker 1: of music itself. For example, what makes one piece of
Speaker 1: music more memorable than another piece? What element does repetition
Speaker 1: play when it comes to making a masterpiece? Was the
Speaker 1: relationship between music, which, when you get down to it,
Speaker 1: really is just math and motion and human perception. And
Speaker 1: I could do an entire episode on Chi's work and
Speaker 1: what her team developed and how they set out to
Speaker 1: design this automated system to analyze music, but that's gonna
Speaker 1: have to wait for a later episode. For now, it's
Speaker 1: just important to understand the music is something that we're
Speaker 1: able to experience in a level that machine just cannot. Now,
Speaker 1: when we come back from the break, we're going to
Speaker 1: listen in on an interview that Noel Brown had with
Speaker 1: Alexander Lurch and learn more about musical analysis and music generation.
Speaker 1: But first let's take a quick break to thank our sponsor. Now,
Speaker 1: Like I said the top of the show, earlier this year,
Speaker 1: in Producer Extraordinary, Noel Brown took a trip to mog Fest,
Speaker 1: which was a you know, it's a conference about music
Speaker 1: and technology and science and lots of other awesome stuff,
Speaker 1: and he got to speak with a music analysis expert,
Speaker 1: Alexander Larch And what follows is their conversation. So as
Speaker 1: a bit of a layman, I interpret a lot of
Speaker 1: what you do in the field of like generative music.
Speaker 1: Is that kind of along the right lines. So um,
Speaker 1: I would say my book may kind of lead to
Speaker 1: generative music, but what I'm actually currently focusing on is
Speaker 1: more analyzing music, so figuring out what's going on in
Speaker 1: the music. So, um, it might start with you just
Speaker 1: have an audio signal and you want to know, okay,
Speaker 1: what is the temple, what is the what is the key,
Speaker 1: what is the hook line, what is the base doing?
Speaker 1: What is the mood of this piece of music? And
Speaker 1: that is when trying to apply artificial intelligence and signal
Speaker 1: processing methods to get this information to extract this inflammation
Speaker 1: from the signal. So that's something like the hit factories
Speaker 1: in Sweden would be all about, you know what they're
Speaker 1: all about, Like it seems that they take a very
Speaker 1: analytical approach to writing pop songs, where you know, they've
Speaker 1: got people that are experts in hooks, they have people
Speaker 1: that are experts in versus, and they have all these
Speaker 1: kind of human algorithms on like how long everything needs
Speaker 1: to play for in order to elicit the proper response.
Speaker 1: Is it sort of along those lines as well, yes,
Speaker 1: and so so you you want to find out, um,
Speaker 1: what kind of makes the songs successful and this might
Speaker 1: have really many many different factors impacting that. Right. So
Speaker 1: there's the structure, of course, but there's there's so many
Speaker 1: other dimensions here that it's really hard to nail it down.
Speaker 1: So using using the computer to analyze this, we try
Speaker 1: to find out more about what's going on and maybe
Speaker 1: identifying these little things that might make something popular or
Speaker 1: might give you goose bumps, or something that an example
Speaker 1: or something that maybe one wouldn't expect might accomplish something
Speaker 1: like that, or just just like an element that maybe
Speaker 1: isn't so obvious to the average listener. It's okay, let
Speaker 1: me let me think. Like it's it's hard to come
Speaker 1: up with a very good example that would be surprising
Speaker 1: to everybody. But it's definitely the combination of tiny things
Speaker 1: like maybe intonation that is somehow a little bit off,
Speaker 1: so you would say, or timing is a very obvious thing.
Speaker 1: If something grooves or not it might have the same rhythm,
Speaker 1: it might really impact you on a on a completely
Speaker 1: different level. Right, So these are examples that are maybe
Speaker 1: not surprising, but but still um point to the direction. Yeah,
Speaker 1: is it maybe an element of human human human interaction?
Speaker 1: Like I think things are too quantized, it's maybe less emotional,
Speaker 1: whereas when people enter the notes by hand and they're
Speaker 1: a little bit imperfect, or for example, the singer Adele,
Speaker 1: there was an article about how she sort of slides
Speaker 1: into her notes and that gives you goose bumps because
Speaker 1: it's got this human quality where you sense that raw
Speaker 1: human emotion in the same way. Maybe someone who does
Speaker 1: electronic music makes mistakes and leaves them in and that's
Speaker 1: what kind of makes it more approachable. Absolutely. I mean,
Speaker 1: one thing you have to keep in mind is that
Speaker 1: it's all jover and artists dependent as well, right, so
Speaker 1: there's there will definitely never be a formula. So if
Speaker 1: you want to have goose bumps, just do that and
Speaker 1: then it looks right, So you can always analyze in retrospect. Okay,
Speaker 1: this artist has this specific thing thing that he or
Speaker 1: she does and that makes things so so um fascinating
Speaker 1: or also that makes you hooked on that, But that
Speaker 1: might not work for a different genre or for a
Speaker 1: new song, right, especially because it's also about expectation and
Speaker 1: what you already know. So um, I can maybe let
Speaker 1: a computer compose something in Mozart style, right, and it
Speaker 1: might be a really good motor piece, but that doesn't
Speaker 1: mean it really gets you as a listener because you
Speaker 1: have heard so many Mozart pieces and the original will
Speaker 1: still be better. It's it's always an imitation, right, so
Speaker 1: so then it might actually miss something there, right, Even
Speaker 1: if the composition itself is very much like Mozart did it, well,
Speaker 1: so is the end product of your research to make
Speaker 1: computers better at doing this or are you just interested
Speaker 1: in kind of you know, breaking down pieces of music
Speaker 1: and to their based elements. So at the moment, I'm
Speaker 1: doing exactly that, I'm breaking it down. I I want
Speaker 1: to be able to let a computer transcribe what's going
Speaker 1: on in the music. I want to understand maybe on
Speaker 1: a perceptional level. So what makes what parameters that you
Speaker 1: can objectively extract from the audio signal? Um? What impact
Speaker 1: might they have on the listener? Right? So so how
Speaker 1: does the listener react to certain um specific characteristics of
Speaker 1: the music. But this knowledge is then also can most
Speaker 1: definitely be used to actually generate new music, um, following
Speaker 1: specific rules that you have extracted from the music and
Speaker 1: then create something new. And this is what my colleague
Speaker 1: Gil Weinberg woks a lot on with his robots that
Speaker 1: make music. Okay, tell me more about that. And let's
Speaker 1: not he the mr what it was interested? Right? Yeah?
Speaker 1: So so there's um he has a robot called him On.
Speaker 1: So she's a marimba playing robot. Um. So what Also,
Speaker 1: my my colleague is a lot into jazz, so Simon
Speaker 1: plays also a lot of jazz UM. So there's a
Speaker 1: lot of um interaction on the stage with the live musicians,
Speaker 1: and the question answer games between what what Simon plays
Speaker 1: on the marimba and what the musician then plays, and
Speaker 1: so it's it's constantly analyzed what's being what's being played,
Speaker 1: and then the robot improvises or tries to um give
Speaker 1: some answers to that jazz. I mean, you have to listen,
Speaker 1: you have to be able to follow the leads that
Speaker 1: you're you know, fellow musicians are putting out there, otherwise
Speaker 1: you're not any good exactly. This whole interaction thing is
Speaker 1: is part of the of the research obviously, and it's
Speaker 1: not only the music, right, it's only it's also just
Speaker 1: just it's eye contact and so on. So that's why
Speaker 1: this robot, even if it doesn't make any sound, has
Speaker 1: actually ahead where where she can look at specific musicians
Speaker 1: um and not her head and so on. So you see,
Speaker 1: you kind of can interact with the robot. So this,
Speaker 1: this human robot interaction is part of the research as well. Fascinating.
Speaker 1: What can you describe the difference between an algorithm that
Speaker 1: does what you're talking about and analyzes music and one
Speaker 1: that might create generative music. It seems like there's sort
Speaker 1: of a crossover between the two, and I'm just I
Speaker 1: just was probably you could kind of like spell that
Speaker 1: out a little bit for us. So, UM, in essence,
Speaker 1: the the algorithm that analyzes music is kind of the
Speaker 1: information you gain from that algorithm has to feed the
Speaker 1: generative algorithm. So, for example, you cannot compose something in
Speaker 1: classical style if you don't know classical style, right, so
Speaker 1: you have to learn it from data. That is the
Speaker 1: analysis part, and then you try to infer models from that. Right.
Speaker 1: So you you have all this data, you have you know, um,
Speaker 1: you have structural data, you have voice leading, you have
Speaker 1: maybe intonation if it's about performance, and then you try
Speaker 1: to fix this data into rules, and these rules then
Speaker 1: would generate music, for example, jazz improvisation or something that.
Speaker 1: So Brian you know, has has been kind of delving
Speaker 1: into generative music lately, and it's actually really interesting. There's
Speaker 1: a BBC documentary of him kind of showing his methods
Speaker 1: and he's just using logic and he has these little
Speaker 1: kind of nodes I guess you could call on the
Speaker 1: scripts or whatever that can set rules for like a
Speaker 1: drum part or something like that where it will say,
Speaker 1: subdivide every other whatever, like any number of things that
Speaker 1: you could input like that. Um, I guess are we
Speaker 1: at a place where that's still just kind of a
Speaker 1: gimmick or are we Are we really trying to recreate
Speaker 1: a human mind creating music or is it just kind
Speaker 1: of a different animal altogether, you know what I mean? Like,
Speaker 1: I'm wondering, are we really trying to have AI that
Speaker 1: can compose mozart, or that can place a producer or
Speaker 1: replace a songwriter, or is it just sort of like
Speaker 1: its own thing that's fascinating in and of itself. So
Speaker 1: I don't think that the goal here is to replace musicians,
Speaker 1: but I think it's um from a research perspective, Um,
Speaker 1: giving a machine creativity is a really fascinating topic, right,
Speaker 1: So is it possible if you just have something algorithm driven, um,
Speaker 1: that it actually creates something new that it hasn't seen before. Right?
Speaker 1: So um I UM, I wouldn't be worried about being replaced,
Speaker 1: although I mean I could see in the future, like
Speaker 1: for example, generating elevator music, right, UM that that I
Speaker 1: can easily see being automatically generated in the future. Um.
Speaker 1: And there, yes, you would actually the AI would actually
Speaker 1: replace the human composer and that in that area. But
Speaker 1: I don't. I don't think that. Um. I think the
Speaker 1: the phenomenon of creativity is still not completely understood. Um.
Speaker 1: And it's with current technologies, it's I think it's really
Speaker 1: hard to get there. I mean, we do use some
Speaker 1: random randomizations and so on, so it generates something that
Speaker 1: you haven't heard before, but well it's random, right, so
Speaker 1: it's not necessarily an act of creativity here. So so
Speaker 1: we're trying to get there, but I think it's still
Speaker 1: a long way to have to create something that is
Speaker 1: really creative. It's not getting a Creativity seems to be
Speaker 1: sort of subjective in and of itself. It's like, does
Speaker 1: creativity mean that it was created by a human? You know,
Speaker 1: like is that exclusively what creativity is? And if we
Speaker 1: have something that is somewhat sentient, can it be creative?
Speaker 1: You know? I would say that the definition of creativity
Speaker 1: is mostly subject based. So there's no godlike instance who says, Okay,
Speaker 1: this is creative and this is not creative. But what
Speaker 1: what it depends on is what the listeners thinks of this, right, um? So,
Speaker 1: which is then in a way makes it really difficult
Speaker 1: to do research because as there's no clear definition of
Speaker 1: what we're measuring. Um, it's it's all the subject driven.
Speaker 1: It's really hard to say, Okay, this is something where
Speaker 1: it's going in the right direction and this is not
Speaker 1: so much. Yeah, but I mean it's so that the
Speaker 1: problem is kind of mentioned learning about official intelligence algorithms,
Speaker 1: they all try to they learned from data, and they
Speaker 1: essentially always try to reproduce something that they learned from
Speaker 1: the data. Right, while real creativity is always thinking all
Speaker 1: of the box. I wanted to be unexpected, like you know,
Speaker 1: uses these algorithms because he wants to surprise himself, but
Speaker 1: he likes to set certain conditions that are appealing to him.
Speaker 1: It's sort of like being the prime mover in the
Speaker 1: situation and then just sort of letting the pieces fall
Speaker 1: where they may at the end of the day. But
Speaker 1: you are sort of still putting yourself into the equation.
Speaker 1: But then you are hoping for unexpected results to surprise yourself.
Speaker 1: And this is definitely one very good way of dealing
Speaker 1: with that. Right because you you have some kind of
Speaker 1: random components there, Um, you don't trust everything that is
Speaker 1: being output. It right, So, but something might be good,
Speaker 1: So you generate a lot of variations of of what
Speaker 1: you might want to achieve, and then you just pick
Speaker 1: something that that really bokes and then you um use
Speaker 1: this as a starting point from where you want to
Speaker 1: go to where you want to go. I mentioned elevator music,
Speaker 1: and I get that for sure, but aren't they already
Speaker 1: using generative music and video games where they have to
Speaker 1: have music constantly playing And obviously it would take ages
Speaker 1: for a single person to compose, you know, hundreds of
Speaker 1: hours of music. And I know there are cues in
Speaker 1: games that are composed, but then there are parts where
Speaker 1: you're maybe wandering around and like the you know RPG
Speaker 1: type game and it's sort of ambient music. It just
Speaker 1: seems to morph and change, right, I mean, this is
Speaker 1: but this is rule based as far as I know.
Speaker 1: I'm I'm far from being an expert in in what
Speaker 1: really happens in these game engines, but my understanding is
Speaker 1: that they define specific states, um and then they have
Speaker 1: certain rules for either looping something, looping specific loops or
Speaker 1: just generating some some more atmospheric background tones within a
Speaker 1: palet or within like a scale or something that's you know,
Speaker 1: but I'm I'm I'm pretty sure that this is not
Speaker 1: necessarily automatically generated. I mean, there might be randomness in there,
Speaker 1: but I think it's basically rule based. So somebody during
Speaker 1: the development specified, okay in this state do something like this.
Speaker 1: How do you think that technology will shape music over
Speaker 1: the next ten twenty years. I mean, obviously, we're at
Speaker 1: a conference festival that is very much involved in the
Speaker 1: connection between technology and music. I love it. I think
Speaker 1: it's amazing. There are some people that are kind of
Speaker 1: freaks out, But I wonder what you think about, like,
Speaker 1: where's it going? Oh, well, that's that's obviously very hard
Speaker 1: to answer. I mean, I mean, so, okay, let me
Speaker 1: start historically, right, so, so technology and music they have
Speaker 1: always interacted very closely. Right, So, there's actually genres who
Speaker 1: would not which would not be there without the technology
Speaker 1: technology exactly, so the electric guitar rock and roll wouldn't
Speaker 1: have happened with all the electric guitar, and the electric
Speaker 1: guitar was in essence and engineering effort, right synthesizer. Obviously,
Speaker 1: we are here at mook Fest. Um so, so there
Speaker 1: was always close interaction between technology, um so. Um what
Speaker 1: the trends that I currently see, and they are not
Speaker 1: really surprising, I guess, but I think that, um, the
Speaker 1: interaction of the performer with any kind of sound generation
Speaker 1: of music generation will will um grow more cohesive. So
Speaker 1: any kind of controller will be easier to use and
Speaker 1: and uh, it will also be easier to use for
Speaker 1: everybody to create music. And this is definitely a trend
Speaker 1: you already see with d J apps and so on,
Speaker 1: where they automatically create matchups for you and and all
Speaker 1: this stuff. Um, it's this is this is definitely going
Speaker 1: to happen that the user will be even if they
Speaker 1: have no music background, will be able to create music
Speaker 1: in a way that that makes sense. It might only
Speaker 1: be loop based for now, but there's a lot of
Speaker 1: possibilities here. Um, I see all the possibilities in more
Speaker 1: crowd based approaches to this. Right, So, um, what happens
Speaker 1: if you put a hundred people into a room and
Speaker 1: give them, I don't know, an app or something that
Speaker 1: they can control and then they make music together? Neural
Speaker 1: network music exactly. And and there's also in this context
Speaker 1: there's new forms how artists can communicate with their fans. Right,
Speaker 1: so you could release something that is actually interactive, So
Speaker 1: so fans could, in the easiest form could vote on something,
Speaker 1: but maybe some more complex input would shape the music
Speaker 1: and outcome there. So I think these are very very
Speaker 1: interesting forms where you already see the seats in what's
Speaker 1: currently happening, um, and I think this will definitely evolve.
Speaker 1: Knowl and Lurch makes some great points about the subtleties
Speaker 1: of music and analysis as well as the potential for
Speaker 1: their future. And when we come back, we'll talk more
Speaker 1: about generating music from a computational standpoint. Generating music, like
Speaker 1: musical analysis, is a non trivial task. How do you
Speaker 1: program a computer so that it might dynamically create, esthetically
Speaker 1: leasing measures of music without becoming too repetitive or boring,
Speaker 1: or straying too far away from a melody line to
Speaker 1: sound like anything other than just random series of notes. Now,
Speaker 1: some music, maybe even a lot of music, is written
Speaker 1: very deliberately. You know, your painstakingly sitting down and figuring
Speaker 1: out what chord comes next, when should you put in
Speaker 1: the key change, how many times should you repeat the chorus.
Speaker 1: It's not as if some mythical muse has reached down
Speaker 1: to touch the musician's brain and create the song fully formed.
Speaker 1: But there have been attempts by humans to create music
Speaker 1: from an almost engineering perspective, so that it almost it
Speaker 1: almost feels like you're taking the artistry out. That's not
Speaker 1: entirely fair. I don't really believe that is so, but
Speaker 1: there do. There are some songs out there that were
Speaker 1: created by committee, and you could argue that some of
Speaker 1: them perhaps seem to have less merit to them than others. Now,
Speaker 1: there's some commit the design music that is amazing for
Speaker 1: reasons that are difficult to put into words. For example,
Speaker 1: in n Dave Soldier, a composer, worked with two artists,
Speaker 1: Komar and Melamid, to create what they titled the Most
Speaker 1: Unwanted Song. They conducted a public survey to find out
Speaker 1: what people most liked and hated in music, and then
Speaker 1: they created two different songs that incorporated many of those elements.
Speaker 1: The ones that included the lowest scoring elements became part
Speaker 1: of the Most Unwanted Song. And it's a song that
Speaker 1: lasts about twenty minutes. It's incredibly long. It's a song
Speaker 1: that includes accordion, bagpipes, children's voices, and opera singer rapping,
Speaker 1: and also incorporated advertising. It's gloriously awful and it sounds
Speaker 1: like this now. They also did the most Wanted Music,
Speaker 1: and they created a song that incorporated the elements that
Speaker 1: the survey takers identified as being the most pleasant components
Speaker 1: of music. The result is something that would likely put
Speaker 1: Kenny g into a coma. It's listening so easy you
Speaker 1: don't even know you're listening. It's a shout out to
Speaker 1: Peter shik Ali right there. I actually think that this
Speaker 1: song is worse than the most Unwanted Song, but take
Speaker 1: a listen the world. Both examples illustrate the power of
Speaker 1: music analysis, as well as how it can easily be
Speaker 1: misinterpreted or misused, which can create I think we can
Speaker 1: all agree horrific results. But neither of those pieces were
Speaker 1: actually generated by computers. That was all the work of
Speaker 1: human beings. Human beings with a wonky sense of humor,
Speaker 1: but still human. And you might think that the first
Speaker 1: computer generated music must have come a decade or so later.
Speaker 1: I mean, the Unwanted Song and Wanted Song both came
Speaker 1: out nine, but now was late for computer generated music.
Speaker 1: The first actual piece written by computer was the Iliac
Speaker 1: Sweet for String Quartet, created in nineteen fifty seven. This
Speaker 1: was the work of Learn Hiller, a composer, and Leonard Isaacson,
Speaker 1: a mathematician, and their approach was fairly straightforward. They created
Speaker 1: a program that would generate pseudo random integers, which in
Speaker 1: turn would represent important information with regards to musical composition
Speaker 1: such as pitch, rhythm, dynamics, and other factors. This processed
Speaker 1: information would then go through a pass on a filter,
Speaker 1: and that filter would force the data to follow rules
Speaker 1: of composition, so it sort out anything that went outside
Speaker 1: of the rules of composition and anything that was when
Speaker 1: then the rules would get a pass and the resulting
Speaker 1: piece of music for a string quartet sounds a bit experimental,
Speaker 1: but it doesn't exactly sound mechanical. It sounds kind of
Speaker 1: like this. Other experiments and music generation followed, but they
Speaker 1: all depended pretty heavily on computers working within relatively strict
Speaker 1: sets of rules, with a good deal of human guidance
Speaker 1: along the way, and of course the computers had no
Speaker 1: actual understanding of music. You could program in rules for
Speaker 1: different musical genres and computers can do that. That's what
Speaker 1: computers do. They're really good at following rules, but the
Speaker 1: machines have no way of knowing why those rules exist
Speaker 1: or what sort of effect those rules have on the
Speaker 1: music itself. Computer scientists have created some interesting experiments to
Speaker 1: build music generators. For example, Matt Vitelli and Erin Naiebe
Speaker 1: built software that analyzed a piece of music by Medean,
Speaker 1: a French DJ, from the day on I suppose I
Speaker 1: apologize my Francie is uh not very good. The software
Speaker 1: analyzed Medeans work and then attempted to replicate it. It
Speaker 1: used recurrent neural networks an attempt to capture the essence
Speaker 1: of the music and make something similar. The neural network
Speaker 1: learned with every iteration of music uh, and learned how
Speaker 1: to more closely mimic the style, So when it first
Speaker 1: started it sounded like pure noise. It took two thousand
Speaker 1: iterations before it generated something that resembled a song more
Speaker 1: than noise. But it shows that these learning algorithms are
Speaker 1: able to start focusing on what those elements are that
Speaker 1: represent meaningful information versus meaningless information. So would this eventually
Speaker 1: be able to create its own music if you were
Speaker 1: to say, said it to listening to a radio station
Speaker 1: for long enough. Who's to say? Over at Google, the
Speaker 1: Brain team is working on a ton of different projects
Speaker 1: related to machine learning and artificial intelligence, including exploring opportunities
Speaker 1: for computer generated music. This falls under something called the
Speaker 1: Magenta Project, and the project has two purposes. The first
Speaker 1: is to experiment with machines creating different forms of art automatically,
Speaker 1: including music. The second purpose is to foster a community
Speaker 1: of artists and programmers to find new and interesting ways
Speaker 1: to use this technology that Google has created. On the
Speaker 1: official page for Magenta, Douglas Eck points out that artists
Speaker 1: have always found innovative ways to put technology to use
Speaker 1: beyond what the creators had in mind, and that's where
Speaker 1: true innovation lies. So in other words, when you create
Speaker 1: an electric guitar for the first time, you're probably not
Speaker 1: anticipating the way Jimmy Hendrix is going to play that
Speaker 1: decades later. So artists have been able to take things
Speaker 1: that people have created and move it beyond even the
Speaker 1: creator's expectations. That's kind of what they're hoping over at
Speaker 1: the Magenta Project. Ck goes on to point out that
Speaker 1: short form machine generated music can be quite effective, and
Speaker 1: it's been around for a while. There are generators out
Speaker 1: there that can make short songs essentially are short pieces
Speaker 1: of music. But if you increase the duration requirement, if
Speaker 1: you require the music to last longer, you start running
Speaker 1: into the limitations of the technology. They start to become
Speaker 1: more apparent, and it becomes clear that machines aren't really
Speaker 1: good at sustaining a long term narrative in any format.
Speaker 1: The Magenta project isn't just a single approach. It's not
Speaker 1: like a group of folks who are just working on
Speaker 1: one set of algorithms. Think of it more like a
Speaker 1: platform or a list of assets, a list of available
Speaker 1: uh bits and pieces other people can use, and programmers
Speaker 1: and musicians can build tools out of those pieces for
Speaker 1: generating music. Now, some of those tools may end up
Speaker 1: being way more effective than other tools. Just figuring out
Speaker 1: how to evaluate the abilities of the software could end
Speaker 1: up becoming a challenge. How can you tell if one
Speaker 1: autonomous music generator is quote unquote better than another one.
Speaker 1: Music is pretty subjective and what I might like might
Speaker 1: not be what you like, And there are some qualitative
Speaker 1: elements that we can look at that are pretty difficult
Speaker 1: to to get a conversation going, because if you have
Speaker 1: a very different set of of pros and cons or
Speaker 1: or set of preferences I should say about music than
Speaker 1: I do. Then we might hit a wall. But there's
Speaker 1: some quantitative elements such as the amount of variation in
Speaker 1: a piece and whether the music generated fits whatever genre
Speaker 1: you're aiming for, that you can use those. That's a
Speaker 1: little bit easier because it's a quantitative or more or
Speaker 1: less a quantitative element. But pretty soon you get into
Speaker 1: more subjective territory, and that's where it all breaks down.
Speaker 1: At the moment, machines are better at interpreting and combining
Speaker 1: musical pieces than they aren't creating something entirely new. For example,
Speaker 1: David Cope, who is a professor emerit us at the
Speaker 1: University of California, Santa Cruz, is also a composer, launched
Speaker 1: a project called Experiments in Musical Intelligence many years ago
Speaker 1: and use the computer program to analyze various classical composers
Speaker 1: musical work. Then the program would construct new pieces using
Speaker 1: the elements it had analyzed as building blocks for that piece.
Speaker 1: So the program wasn't really writing something entirely new, but
Speaker 1: rather combining found elements in new ways. Now, perhaps in
Speaker 1: the future machines will be able to make art on
Speaker 1: their own with minimal human input, and if that happens,
Speaker 1: we'll likely have to face some tough philosophical questions about
Speaker 1: the nature of art. If a machine doesn't possess self
Speaker 1: awareness or consciousness and really is just a complicated set
Speaker 1: of equations that generate data according to some general rules,
Speaker 1: is its production actually art? Is intent required for it
Speaker 1: to be art? Does the artist have to intend something
Speaker 1: in order for it to be art? If people enjoy
Speaker 1: the work and find it intellectually or emotionally stimulating, does
Speaker 1: that make it real music? If if I like something
Speaker 1: and I find out later on that a computer generated
Speaker 1: it completely from start to finish, does that at all
Speaker 1: lesson the value of that music? Or does the fact
Speaker 1: that I like it mean that it's quote unquote real.
Speaker 1: We're none at the stage right now where those questions
Speaker 1: need urgent answers, but I do think they're really interesting,
Speaker 1: and now it's time that we play our own music
Speaker 1: and get the heck out of here. So if you
Speaker 1: guys have any suggestions for future episodes of tech Stuff,
Speaker 1: right to me. Let me know what you think. Our
Speaker 1: email address is tech Stuff at how stuff works dot com,
Speaker 1: or you can drop me a line on Twitter or Facebook.
Speaker 1: The handle for the show at both of those is
Speaker 1: tech Stuff hsw on Wednesdays and Friday's. I record in
Speaker 1: the studio and you can watch me live on twitch
Speaker 1: dot tv slash tech Stuff. Watch as I struggle for
Speaker 1: words and fail and then head desk and then tell
Speaker 1: Dylan to pause the recording so I can come up
Speaker 1: with something and then start the recording again. You get
Speaker 1: to see the whole thing, so all the stuff that
Speaker 1: gets cut out of the podcasts, you can watch it
Speaker 1: happen live. Sometimes I dance. I hope to see you
Speaker 1: Wednesdays and Fridays at twitch dot tv slash text Stuff
Speaker 1: and I'll talk to you again really soon. For more
Speaker 1: on this and thousands of other topics, because it has
Speaker 1: stop works dot com