WEBVTT

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[SQUEAKING]

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[RUSTLING]

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[CLICKING]

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PROFESSOR: Yeah, we
just did the quiz.

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And that's for people who
are watching this later.

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That's not on the video.

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We're going to do some
introduction to music cognition.

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I don't think we're
going to have time,

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but we can also unlock
voice leading if we do.

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Now I'm going to put this down.

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Great.

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And I'm going to
turn off these--

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no.

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That one's OK.

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Turn off that.

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Is that OK?

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Yeah, that's better.

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Great.

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So music cognition is
one of these things that

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intersects with huge
numbers of parts of music

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in general, with music
theory, with music history,

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with performance, with
performance anxiety--

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all these things.

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But it also has a big impact on
computational music, especially

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computational music theory.

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It sort of formed
this field, and it

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continues to be part of it.

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I just want to give
a little introduction

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from a professor
of music cognition

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at Northeastern down the street
and also Wesleyan Psyche Loui.

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Just a couple of minutes
while I also checked

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to make sure I have the--

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well, we'll see if the
sound's still working.

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[VIDEO PLAYBACK]

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- Consider, for instance,
this musical example.

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[PIANO CHORDS PLAYING]

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All right.

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You might say that sounds nice
and normal, kind of like saying,

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I took the tea here today.

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What about this?

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[PIANO CHORDS PLAYING]

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[DISCORDANT CHORD]

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Right.

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If you think that
sounded normal,

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you can come talk
to me afterwards,

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and we might sign you up
for that tone deafness study

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we're doing.

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[LAUGHTER]

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So when you heard
that last chord,

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your brain does a
double take, right?

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There's something about it.

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That's like saying, I took
the tea here, octopus.

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Nothing wrong with
octopus, but it just

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doesn't fit the context of
what happened before it.

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It doesn't fit the grammar.

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Now, this double take
that your brain does

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can be measured using
electrical potentials

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on the surface of the scalp.

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This is a picture
of my mom getting

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her brain potentials recorded.

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And she's got 64 electrodes
on her cap there.

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And what those do is make
recordings like this.

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And so on the left, I'm
showing you brain responses

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to expected and
unexpected musical chords.

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And on the right,
I'm showing you

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the difference between
expected and unexpected

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on the surface of the scalp.

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So there's a bird's-eye
view of the scalp.

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So right away, you can see that
200 milliseconds after the onset

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of the unexpected chord, your
brain is doing its double take,

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or saying, oh, that
was unexpected.

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And 500 milliseconds, you
get the brain saying, oh,

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how do I integrate that
into what happened before?

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So this is telling us
with millisecond accuracy

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that we know about music.

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There is something
about our brains

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that is very sensitive to what's
grammatical in Western music.

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So the question is, where
does this knowledge come from?

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How do we come to
know what we know?

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So to answer that
question, we again

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have to go all the way
back to the ancient greeks.

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Pythagoras found that if
two strings are being played

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together, where one string is
twice the length of the other,

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those two sound good together.

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They sound consonant.

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So this 2-to-1 frequency ratio
is what supposedly brought us

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closer to the Greek gods.

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In fact, the word
symphony originally

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means vibrating in
perfect harmony using

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these mathematical
integer ratios.

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So this 2-to-1 frequency ratio
is true of music all around

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the world.

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Now, different cultures divide
that 2-to-1 frequency ratio

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in different ways.

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In our culture, the
equal-tempered Western chromatic

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scale divides them in 12 steps.

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So this is how it sounds.

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[ASCENDING SCALE PLAYING]

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OK.

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Then these two guys
came along and said,

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does it have to be this way?

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Why 2 to 1?

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Why not 3 to 1?

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So the Bohlen-Pierce scale is
based on a 3-to-1 frequency

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ratio.

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And within that, we've got
13 logarithmic divisions

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of that scale.

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So you still get some
mathematical integer ratio.

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So the Greek gods are
not offended here.

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But what this sounds
like is completely

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different from Western
or other types of music.

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[ASCENDING SCALE PLAYING]

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So this is a really
powerful approach

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to find out what people know
about music in the laboratory.

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So we can be pretty sure
that people have never

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heard this music before.

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But when they come
in, then they can

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listen to this
music for a while,

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and then we can measure how they
come to know what they know.

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So I'm going to play you
for about a minute a snippet

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of a piece by It's
called "Reminiscences."

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And it's written in the
Bohlen-Pierce scale,

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just so you get an idea.

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[STEPHEN YI, "REMINISCENCES"]

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So this really is kind of
an otherworldly new musical

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experience that we're
entering here and in our lab.

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What we wanted to do was
figure out how people

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learn this new musical system.

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So we have these well-controlled
melodies that people listen to

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for about half an hour.

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[ELECTRONIC TONES PLAYING]

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So you listen to these
things for half an hour.

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And they're they're
defined using

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rules and principles or
grammatical structures

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that we've defined ourselves.

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And then the question
is, what can people

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learn from this new
musical experience?

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The first thing we found
was that memory increases

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with repetition.

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Turns out also that preference
increases with repetition.

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So what we're seeing here is
the beginning of musical taste.

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The more you listen
to something,

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the more you begin to like it.

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But what I'm interested
in is how learning occurs.

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And it turns out that
learning does not

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occur with repetition
but with variability.

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In other words, the
more different ways

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you tell people
something, the more people

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are able to infer the underlying
structure of what you tell them

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and then to generalize
those to new instances

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of the same grammar.

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Now our question becomes now--

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[VIDEO PLAYBACK]

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PROFESSOR: And that's where
I stop for here, because we

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can look at that online.

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And I want you to just get
as excited for our two guest

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speakers who are going to
be talking on Zoom on Friday

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and after after
break here on campus.

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So we're going to
be talking primarily

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in the next couple of
classes about the work.

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Well, first, Claire Arthur is
going to present her own work,

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and then we'll be talking
about two major figures

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in music cognition.

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One is Carol Krumhansl
and her landmark book,

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Cognitive Foundations
of Musical Pitch,

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which is all about, how
do we process pitch?

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How do we think, how do we
put it into particular areas?

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And so that's pretty
important for music cognition.

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But it's also formed her work
on this, formed the basis

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for every computational--

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or almost every-- yeah, I
can't say the wrong things--

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for the most widely-used key
detection and key algorithm

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finding mechanisms.

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So here, this is our
favorite BWV 66.6,

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looking at different sizes of
amount of memory that we have.

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And we can see that--

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where is it-- at certain
things, A major is coming up.

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And then we're moving
from one key to another

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as our cognition goes.

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So we'll be looking
into her work.

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The second major
figure is David Huron,

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whose name might have appeared
as a secondary author of one

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of the articles that you may
have discussed on your midterm.

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And his landmark book,
Sweet Anticipation, Music

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and the Psychology of
Expectation, you're

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going to have access to this
book totally on the website

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by Friday.

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But I feel like if there's one
thing worth spending your money

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to own in a physical copy,
coming out of this class,

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it's this book.

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First off, it's
not too expensive.

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It's, like, $22 or something.

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But it's just amazing,
the types of things--

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and it talks about his
own work, but mostly, it's

00:08:45.610 --> 00:08:50.230 align:middle line:84%
a collection of the greatest
hits of music cognition also

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by others.

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And so this is a
particular experiment

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that I think's really great.

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Balinese musicians
and American musicians

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were given a Balinese
melody that was there.

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And they were betting
on whether or not--

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I believe it was-- the next
note goes up or down after that.

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And you can see the
Balinese musician--

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and so this is how
uncertain they are.

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So lower is more certain.

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The Balinese
musicians, when they

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hear this traditional Balinese
thing, they're just doing great.

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They're making money,
head over heels.

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And then, boom,
uncertainty goes up

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when it goes into a
different direction,

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because this was a crafted
melody designed to do that--

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and so being able to check
out things like that.

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We'll also be
looking a little bit

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at rhythm and
rhythmic uncertainty

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and the types of things that
music cognition might tell us

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for how to generate rhythm.

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So this is a this is a
slightly complex graph.

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But actually, I think
it's the simplest

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way to explain this diagram.

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Say you have three notes.

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Whatever three notes they are,
say that their total durations

00:10:09.720 --> 00:10:12.450 align:middle line:90%
sum up to 1.

00:10:12.450 --> 00:10:16.560 align:middle line:84%
And then you can figure out
how much, what percentage,

00:10:16.560 --> 00:10:20.110 align:middle line:84%
or what fraction of the duration
each of the notes takes up.

00:10:20.110 --> 00:10:21.200 align:middle line:90%
Oh gosh.

00:10:21.200 --> 00:10:21.700 align:middle line:90%
Let's see.

00:10:21.700 --> 00:10:22.430 align:middle line:90%
That's 1/2.

00:10:22.430 --> 00:10:22.930 align:middle line:90%
What's that?

00:10:22.930 --> 00:10:24.860 align:middle line:90%
That's 3/8.

00:10:24.860 --> 00:10:25.360 align:middle line:90%
Thank you.

00:10:25.360 --> 00:10:27.900 align:middle line:90%
And that's 1/8.

00:10:27.900 --> 00:10:31.470 align:middle line:84%
And then you can place each
of them between 1 and 0

00:10:31.470 --> 00:10:32.170 align:middle line:90%
on this chart.

00:10:32.170 --> 00:10:34.230 align:middle line:84%
And this triangular
chart lets you

00:10:34.230 --> 00:10:39.010 align:middle line:84%
show the proportion of each of
three things at the same time.

00:10:39.010 --> 00:10:42.660 align:middle line:84%
And one of the things
that comes out of this

00:10:42.660 --> 00:10:47.070 align:middle line:84%
is that there's not one
point where people perceive--

00:10:47.070 --> 00:10:50.440 align:middle line:84%
so there's one point where
it should be exactly, right?

00:10:50.440 --> 00:10:57.360 align:middle line:84%
So 2-1-1 should be exactly
here on the 25% axis for one,

00:10:57.360 --> 00:11:02.320 align:middle line:84%
on the 25% axis for another,
and the 50% for a third one.

00:11:02.320 --> 00:11:03.730 align:middle line:90%
So it should be right here.

00:11:03.730 --> 00:11:07.060 align:middle line:90%
And 3-1-4 in this way--

00:11:07.060 --> 00:11:10.390 align:middle line:84%
3/8, 1/8, 4/8 should
be right here.

00:11:10.390 --> 00:11:13.800 align:middle line:84%
And one of the things that
cognitive studies has shown

00:11:13.800 --> 00:11:16.900 align:middle line:84%
is that people perceive things
a little bit differently.

00:11:16.900 --> 00:11:22.410 align:middle line:84%
So this entire region of playing
is perceived in this experiment

00:11:22.410 --> 00:11:25.690 align:middle line:90%
as the 2-1-1 or 1/2, 1/4, 1/4.

00:11:25.690 --> 00:11:31.410 align:middle line:84%
Whereas the region that was
perceived as 3-1-4 actually does

00:11:31.410 --> 00:11:38.130 align:middle line:84%
not include the mathematically
correct 3/8, 1/8, 4/8.

00:11:38.130 --> 00:11:40.380 align:middle line:84%
And so we'll look
at experiments that

00:11:40.380 --> 00:11:44.867 align:middle line:84%
try to chart the whole landscape
of rhythmic perception.

00:11:44.867 --> 00:11:46.950 align:middle line:84%
And you can imagine that
that could also be useful

00:11:46.950 --> 00:11:49.080 align:middle line:90%
if you're generating music--

00:11:49.080 --> 00:11:52.343 align:middle line:84%
that when you generate patterns
and you want people to hear them

00:11:52.343 --> 00:11:54.010 align:middle line:84%
and you don't want
it to sound computer,

00:11:54.010 --> 00:11:55.620 align:middle line:90%
you might do something else.

00:11:55.620 --> 00:11:59.740 align:middle line:84%
But the title of the book's
called Sweet Anticipation.

00:11:59.740 --> 00:12:04.230 align:middle line:84%
And by far, I think the most
interesting for me part of this

00:12:04.230 --> 00:12:08.920 align:middle line:84%
is studying anticipation,
expectation, and surprise.

00:12:08.920 --> 00:12:13.340 align:middle line:84%
And so I was playing when you
all walked in a piece by Peter

00:12:13.340 --> 00:12:17.790 align:middle line:84%
Schickele, "PDQ Bach,"
fake Bach humorist.

00:12:17.790 --> 00:12:21.270 align:middle line:84%
And what I love is a
little experiment here,

00:12:21.270 --> 00:12:23.630 align:middle line:84%
where we're going to
be playing something

00:12:23.630 --> 00:12:25.320 align:middle line:90%
that breaks expectations.

00:12:25.320 --> 00:12:27.510 align:middle line:84%
Now, you'll hear some
laughter on the soundtrack.

00:12:27.510 --> 00:12:29.463 align:middle line:84%
That's probably
also physical things

00:12:29.463 --> 00:12:31.880 align:middle line:84%
are happening on stage that
we can't see because it's just

00:12:31.880 --> 00:12:33.420 align:middle line:90%
an audio version.

00:12:33.420 --> 00:12:37.820 align:middle line:84%
But listen to what people
find funny musically

00:12:37.820 --> 00:12:41.613 align:middle line:84%
and how it has to do with
the breaking of expectations.

00:12:41.613 --> 00:12:44.280 align:middle line:84%
So you're going to be listening
to a little thing this is about.

00:12:44.280 --> 00:12:47.460 align:middle line:84%
We're going to start about 20
seconds before this figure.

00:12:47.460 --> 00:12:49.590 align:middle line:84%
So there's going to be
a bunch of other things.

00:12:49.590 --> 00:12:54.920 align:middle line:84%
And maybe just look for a second
yourself on what expectation

00:12:54.920 --> 00:12:58.217 align:middle line:90%
might be broken here.

00:12:58.217 --> 00:13:00.300 align:middle line:84%
[PETER SCHICKELE, "CONCERTO FOR
HORN AND HARDART"]

00:13:00.300 --> 00:13:02.260 align:middle line:90%
[STRING INSTRUMENTS PLUCKING]

00:13:02.260 --> 00:13:06.170 align:middle line:90%
[VIOLINS PLAYING]

00:13:06.170 --> 00:13:06.670 align:middle line:90%


00:13:06.670 --> 00:13:09.610 align:middle line:90%
[LAUGHTER]

00:13:09.610 --> 00:13:11.080 align:middle line:90%
[STRING INSTRUMENTS PLUCKING]

00:13:11.080 --> 00:13:14.020 align:middle line:90%
[VIOLINS PLAYING]

00:13:14.020 --> 00:13:16.960 align:middle line:90%
[LAUGHTER]

00:13:16.960 --> 00:13:19.252 align:middle line:90%
[STRING INSTRUMENTS PLUCKING]

00:13:19.252 --> 00:13:21.210 align:middle line:84%
You hear the accompaniment
in the wrong places.

00:13:21.210 --> 00:13:23.117 align:middle line:90%
[VIOLINS PLAYING]

00:13:23.117 --> 00:13:26.111 align:middle line:90%
[LAUGHTER]

00:13:26.111 --> 00:13:27.608 align:middle line:90%


00:13:27.608 --> 00:13:30.602 align:middle line:90%
[VIOLINS PLAYING]

00:13:30.602 --> 00:13:33.097 align:middle line:90%
[LAUGHTER]

00:13:33.097 --> 00:13:35.093 align:middle line:90%
[VIOLINS PLAYING]

00:13:35.093 --> 00:13:38.586 align:middle line:90%
[LAUGHTER]

00:13:38.586 --> 00:13:42.079 align:middle line:90%
[VIOLINS PLAYING LONG NOTE]

00:13:42.079 --> 00:13:45.572 align:middle line:90%
[LAUGHTER]

00:13:45.572 --> 00:13:50.562 align:middle line:90%


00:13:50.562 --> 00:13:52.558 align:middle line:90%
[VIOLINS PLAYING]

00:13:52.558 --> 00:13:54.070 align:middle line:90%
[LAUGHTER]

00:13:54.070 --> 00:14:01.030 align:middle line:84%
Shows a particular expectation
for how things might be going

00:14:01.030 --> 00:14:01.810 align:middle line:90%
and stuff.

00:14:01.810 --> 00:14:02.920 align:middle line:90%
Oh.

00:14:02.920 --> 00:14:07.490 align:middle line:90%
And these are our two guests.

00:14:07.490 --> 00:14:09.087 align:middle line:90%
They're both named Claire.

00:14:09.087 --> 00:14:10.670 align:middle line:84%
They're in alphabetical
order, though.

00:14:10.670 --> 00:14:14.290 align:middle line:84%
Claire Arthur will be coming
first, coming from Georgia Tech,

00:14:14.290 --> 00:14:19.390 align:middle line:84%
and Claire Pelofi coming from
NYU's Music and Audio Research

00:14:19.390 --> 00:14:21.370 align:middle line:90%
Laboratory.

00:14:21.370 --> 00:14:24.430 align:middle line:84%
I'm bringing in guests
in part because this

00:14:24.430 --> 00:14:26.890 align:middle line:84%
is something-- music
cognition is something I think

00:14:26.890 --> 00:14:28.700 align:middle line:90%
is really, really important.

00:14:28.700 --> 00:14:31.390 align:middle line:84%
But it's probably the
weakest topic for me

00:14:31.390 --> 00:14:33.350 align:middle line:90%
as a teacher in this field.

00:14:33.350 --> 00:14:35.920 align:middle line:84%
So you should be getting
this information from people

00:14:35.920 --> 00:14:38.570 align:middle line:90%
who do it much better than me.

00:14:38.570 --> 00:14:42.010 align:middle line:90%
So that's great.

00:14:42.010 --> 00:14:43.610 align:middle line:90%
We have seven minutes left.

00:14:43.610 --> 00:14:48.280 align:middle line:84%
Who wants to program an unlock,
and who wants to go early?

00:14:48.280 --> 00:14:49.340 align:middle line:90%
Unlock?

00:14:49.340 --> 00:14:50.560 align:middle line:90%
Go?

00:14:50.560 --> 00:14:52.160 align:middle line:90%
OK, great.

00:14:52.160 --> 00:14:53.150 align:middle line:90%
Have a great time.

00:14:53.150 --> 00:14:54.470 align:middle line:90%
Have a great break.

00:14:54.470 --> 00:14:57.770 align:middle line:84%
And I don't want to start a new
topic in seven minutes either.

00:14:57.770 --> 00:14:59.830 align:middle line:90%
So have a good-- sorry.

00:14:59.830 --> 00:15:02.270 align:middle line:84%
I'll see you after
break in person,

00:15:02.270 --> 00:15:04.570 align:middle line:90%
but I'll see you all on Friday.

00:15:04.570 --> 00:15:06.600 align:middle line:90%
So thank you.

00:15:06.600 --> 00:15:17.000 align:middle line:90%