Video 7d: Partwise vs. Timewise Polyphonic Representations
Key Takeaways
The video discusses two fundamental ways to represent musical scores: Partwise and Timewise representations, highlighting their advantages and drawbacks in the context of musical analysis and composition.
Full Transcript
hello as the last element in our discussion of musical representations I want to talk about two different fundamental ways that we can represent musical scores TimeWise and partwise and these really matter in anything that has more than one note or more than one independ part going s once and I want to acknowledge I'm going to be using slides from a presentation called notation as composition by William Andrew burnson creator of the B bage notation package one of the ways that we can look at musical notes is as musical scores is as a series of containers so that we can think of uh and part based containers so we have a score and within that score as the first element we have two parts in this case and within those parts we have measures and we might even go further and think that within those measures we have voices but first when we think about dividing a score into parts let's say it's you know the score of um a massive three-hour Opera well you start with you whatever's at the top of the piccolo or something like that and you go with the piccolo part from measure one all the way to the end of the three hours then you go to the second part probably the flute and you go all the way from one to the end and so on and so you have everything in one part from the beginning to the end and it makes it very easy to do the kinds of analysis that look at what happens in the part the other principal way of looking at musical notation representation is you have a score and within that you have all the things that happen at a certain moment so that be getting you know you might say well there's all the clubs and if it's a 10 bar score you might have trouble trouble trouble trouble trouble Alto base base Bas base Bas so on and then you have all the time signature key signatures all the time signatures and then every note everything that happens at the beginning of the piece and then you have everything that happens at the next instant the next time that something happens here and in this particular case when we get to the third instant um which is the E quarter note in the base and then the c u the C eighth note in the E there there is something that's happening in the top part that that g half note that is not represented in that instant but is thought to be continuing from before and in the next instant the there was just a an eighth note with nothing else happening and so this makes it very nice to see what's happening at every attack and you might be able to programmatically go back and see what's happening at every instant but it makes it very hard instead to figure out what's happening from one uh beginning of the score to the End by the way we call these instance uh instance or uh offset moments or you know cuts through hierarchies generally speaking but often we Cally call these salami slices in the process of creating these things salami slicing it's just kind of something that's come out in music 21 we call this process codification and so you'll see the cify happens a lot so which is better part-based containers or time based containers each of these formats has their own POS positive things and drawbacks as I said part-based containers make it very easy for us to follow an entire part from beginning to end an entire line from beginning to end time based containers let us see what chords are happening in a certain moment but very hard to follow Melody but each of them has certain uh certain difficult to encode things for instance in alia staff uh a staff where something uh optional is happening or or you can do this instead of something else what part is that in how do you encode that uh do you put a part with hidden measures all the way up to this moment and hidden measures all the way to the end and how do we indicate that the player could do this so this is something that's very hard to encode when we do part-based representations so instead let's move to time based representations because it doesn't have this they don't have this problem but instead they have other problems where do we put these Grace notes uh in you know in in encoding like this especially when you know we know that not all five of those Grace notes come after that last let's assum it's trouble in base Club after that last B of the sub tuplet within the triplet you know where do we go how do we encode Grace notes do they attach to the note next to them or or what and both of the representations have maj problems when we want to look at things that involve horizontal and vertical at the same time things like voice leading here's a common uh voice leading error in uh in sort of very strict traditional uh harmonic writing where we have um and we have a overlap where the low G crosses the boundary established by the B before and goes up to the C above it which form format makes this possible to see if you're just look at each part alone you see okay there's a b going to an E then we get to the next part there's a g going to a c but you don't see that the G Going to the C overlap the B going to an e on the other hand if we look at it in time based we have a g and a b and we look at that then the next part we see there's a c and an e but it's hard to tell well did the G go to the C did the G go to the E did the you what what happened how's the voice leading happen so these are the problems in musical representations and we'll quite often find that we have to convert from part-based representation to time based representation and back or in the case of a voice leading moment we have to keep both the part and the time based representations in in our head in the musical memory in the representation at the same time in order to find these things I find these problems really cool and hope you guys do too and solving them is is another one of the great choice of this class
Original Description
MIT 21M.383 Computational Music Theory and Analysis Spring 2023
Instructor: Michael Scott Asato Cuthbert
View the complete course: https://ocw.mit.edu/courses/21m-383-computational-music-theory-and-analysis-spring-2023/
YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62vSB2sI0W8lQFKsmS2-A6R
A slightly deeper dive into musical representation in general, but Classical Western Music Notation (CWMN) in particular, with a look at the two major ways of representing polyphonic music: Partwise or Timewise.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRl
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed.
More details at https://ocw.mit.edu/comments.
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from MIT OpenCourseWare · MIT OpenCourseWare · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
21. Post Trade Clearing, Settlement & Processing
MIT OpenCourseWare
10. Financial System Challenges & Opportunities
MIT OpenCourseWare
7. Technical Challenges
MIT OpenCourseWare
3. Blockchain Basics & Cryptography
MIT OpenCourseWare
19. Primary Markets, ICOs & Venture Capital, Part 1
MIT OpenCourseWare
1. Introduction for 15.S12 Blockchain and Money, Fall 2018
MIT OpenCourseWare
Chalk Radio, A Podcast about Inspired Teaching at MIT (Teaser)
MIT OpenCourseWare
Nuclear Gets Personal with Prof. Michael Short (S1:E1)
MIT OpenCourseWare
How Africa Has Been Made to Mean with Prof. Amah Edoh (S1:E2)
MIT OpenCourseWare
Making Deep Learning Human with Prof. Gilbert Strang (S1:E3)
MIT OpenCourseWare
Social Impact at Scale, One Project at a Time with Dr. Anjali Sastry (S1:E4)
MIT OpenCourseWare
Film is for Everyone with Prof. David Thorburn (S1:E5)
MIT OpenCourseWare
Lecture 12: Aircraft Performance
MIT OpenCourseWare
Lecture 3: Learning to Fly
MIT OpenCourseWare
Lecture 13: Interpreting Weather Data
MIT OpenCourseWare
Lecture 21: Weather Minimums and Final Tips
MIT OpenCourseWare
Hand-on, Minds On with Dr. Christopher Terman (S1:E6)
MIT OpenCourseWare
Part 4: Eigenvalues and Eigenvectors
MIT OpenCourseWare
Part 5: Singular Values and Singular Vectors
MIT OpenCourseWare
Part 3: Orthogonal Vectors
MIT OpenCourseWare
Part 2: The Big Picture of Linear Algebra
MIT OpenCourseWare
Part 1: The Column Space of a Matrix
MIT OpenCourseWare
Intro: A New Way to Start Linear Algebra
MIT OpenCourseWare
9. Chromatin Remodeling and Splicing
MIT OpenCourseWare
28. Visualizing Life - Fluorescent Proteins
MIT OpenCourseWare
20. Roth's theorem III: polynomial method and arithmetic regularity
MIT OpenCourseWare
8. Szemerédi's graph regularity lemma III: further applications
MIT OpenCourseWare
19. Roth's theorem II: Fourier analytic proof in the integers
MIT OpenCourseWare
12. Pseudorandom graphs II: second eigenvalue
MIT OpenCourseWare
1. A bridge between graph theory and additive combinatorics
MIT OpenCourseWare
Special Episode: Teaching Remotely During Covid-19 with Prof. Justin Reich
MIT OpenCourseWare
Spring 2020 Update from Dean Rajagopal
MIT OpenCourseWare
S1E7: Unpacking Misconceptions about Language & Identities with Prof. Michel DeGraff
MIT OpenCourseWare
Climate 101 Live
MIT OpenCourseWare
Welcome for Volunteers (for EarthDNA's Climate 101)
MIT OpenCourseWare
Learning to Fly with Drs. Philip Greenspun & Tina Srivastava (S1:E8)
MIT OpenCourseWare
Thinking Like an Economist with Prof. Jonathan Gruber (S1:E9)
MIT OpenCourseWare
2. Cyber Network Data Processing; AI Data Architecture
MIT OpenCourseWare
1. Artificial Intelligence and Machine Learning
MIT OpenCourseWare
2: Resistor Capacitor Circuit and Nernst Potential - Intro to Neural Computation
MIT OpenCourseWare
14: Rate Models and Perceptrons - Intro to Neural Computation
MIT OpenCourseWare
4: Hodgkin-Huxley Model Part 1 - Intro to Neural Computation
MIT OpenCourseWare
18: Recurrent Networks - Intro to Neural Computation
MIT OpenCourseWare
3: Resistor Capacitor Neuron Model - Intro to Neural Computation
MIT OpenCourseWare
15: Matrix Operations - Intro to Neural Computation
MIT OpenCourseWare
13: Spectral Analysis Part 3 - Intro to Neural Computation
MIT OpenCourseWare
16: Basis Sets - Intro to Neural Computation
MIT OpenCourseWare
20: Hopfield Networks - Intro to Neural Computation
MIT OpenCourseWare
8: Spike Trains - Intro to Neural Computation
MIT OpenCourseWare
7: Synapses - Intro to Neural Computation
MIT OpenCourseWare
19: Neural Integrators - Intro to Neural Computation
MIT OpenCourseWare
5: Hodgkin-Huxley Model Part 2 - Intro to Neural Computation
MIT OpenCourseWare
6: Dendrites - Intro to Neural Computation
MIT OpenCourseWare
17: Principal Components Analysis_ - Intro to Neural Computation
MIT OpenCourseWare
12: Spectral Analysis Part 2 - Intro to Neural Computation
MIT OpenCourseWare
11: Spectral Analysis Part 1 - Intro to Neural Computation
MIT OpenCourseWare
9: Receptive Fields - Intro to Neural Computation
MIT OpenCourseWare
10: Time Series - Intro to Neural Computation
MIT OpenCourseWare
1: Course Overview and Ionic Currents - Intro to Neural Computation
MIT OpenCourseWare
The Power of OER with Profs. Mary Rowe and Elizabeth Siler (S1:E10)
MIT OpenCourseWare
More on: RAG Basics
View skill →Related Reads
📰
📰
📰
📰
Beyond HTML: Why the Future of the Web May Be Semantic-First, Not Markup-First
Medium · JavaScript
The Great UX Convergence
Medium · UX Design
Too Many Steps, Too Many Choices: Redesigning the Ryanair Booking Experience
Medium · UX Design
Part 4 — Tags, ratings, and richer rows
Dev.to · Nerd Snipe
🎓
Tutor Explanation
DeepCamp AI