CS 372: Digital Music Processing

Ursinus College, Spring 2023

Table Of Contents

Overview Technology Logistics Readings

Deliverables Schedule Grading

Classroom Environment Collaboration Policy Other Resources / Policies

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Overview

Class Times / Locations

• Monday/Wednesday/Friday, 1:30-2:20PM in Pfahler 106

Student Office Hours

• Monday/Wednesday 3:30PM - 5PM, Pfahler 109
• Friday 10AM - 11AM, Pfahler 215 (Note the room change!)
• Tuesday 7PM - 9PM virtually, by appointment (appointment e-mails will be send weekly on Monday nights)

Prerequisites/Requirements

Math 111 or equivalent, CS 173 or equivalent

Instructor

Christopher J. Tralie

I grew up right around the corner in the Montgomery County and attended Upper Dublin High school (class of 2007). I then did my undergraduate degree in Electrical Engineering at Princeton University and my master's and Ph.D. degrees in Electrical And Computer Engineering at Duke University (heavily studying math and CS along the way). I finally started my dream job at Ursinus College in Fall of 2019! You can read more about my interests on my professional web site. Looking forward to getting to know everyone as we work through this course together!

Course Description

This hands-on course will take a broad overview of how to represent, analyze, and morph/transform digital musical audio with a computer. My goal at a high level is by the end of the semester, students should be able to walk into a poster session at a conference like ISMIR or DAFX and understand roughly what's going on. On the synthesis/transformative end, students will create digital musical instruments, they will learn how to filter and denoise audio, they will create programs such as vocoders (e.g. "talking guitars") and autotuners (i.e. "I Am T-Pain"), and they will implement algorithms to automatically time warp and align different versions, or "cover songs," so that they can be compared side by side, even when the timing is different. On the analysis end, students will train a computer to automatically figure out what notes a person is playing, to "tap a virtual foot" to the beat, to discover repeated sections such as verse/chorus, to identify a song from small clips of audio (the so-called "Shazam algorithm").

In the process of implementing the above algorithms, students will gain familiarity with a fundamental tool known as the Discrete Fourier Transform, which allows us to break audio down into its fundamental components: a bunch of pure sine waves added together. This amazing way of looking at audio will allow us to "put on a different set of glasses" to find patterns and filter out parts of audio more easily.

Learning Goals

1. Develop broad knowledge of analysis and synthesis techniques for digital audio, with an eye towards musical applications.
2. Learn how to incrementally design, implement, and test signal processing pipelines in code.
3. Use mathematical and physical models to synthesize audio and to process messy real-world data.

Learning Objectives

1. Practice patient problem solving by developing comfort with the edit -> compile -> run loop, along with intermediate debugging skills.
2. Implement algorithms based on the Short-Time Fourier Transform for a variety of analysis and synthesis tasks in audio.
3. Implement several basic machine learning pipelines for different audio tasks, such as artist identification, cover song identification, and beat tracking.

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Technology Logistics

We will be using a zoo of technologies in the course, as has become standard in 21st century work environments. Below is a table summarizing what kinds of communications/activities occur via each technology, and below that there are more details on everything. This is admittedly complex, and it will take some getting used to, but it will be worth it once we get it nailed down.

NOTE: I will repeat the same announcements across e-mail and Discord, so you don't have to check all both for announcements.

Class web site (You are here!)	Schedule (Topics, Study Resource Links, Assignment Deadlines) Assignment descriptions
Canvas	Assignment Deadlines, Submission, and Feedback
Discord	Chat/polls during class Announcements Student office hours Class-wise questions and discussions, including anonymous questions Individual chat with Prof. Tralie of a less sensitive nature (e.g. tech support, assignment questions not appropriate for the entire class)
Microsoft Teams	Buddy Collaborations/Screen Sharing Individual chat with Prof. Tralie of a less sensitive nature (e.g. tech support, assignment questions not appropriate for the entire class) Individual chat with Prof. Tralie of a more sensitive/private nature (e.g. questions about grades, academic planning, personal issues) (*)
E-mail	Announcements Individual chat with Prof. Tralie of a more sensitive/private nature (e.g. questions about grades, academic planning, personal issues), though I prefer Microsoft Teams for this and will respond more quickly over Teams Individual chat with Prof. Tralie of a less sensitive nature (e.g. tech support, assignment questions not appropriate for the entire class), though I prefer Microsoft Teams or Discord for this and will respond more quickly over Teams

*: For privacy reasons, anything of a personal nature, and particularly things that have to with educational records (e.g. grades), need to be kept within Ursinus sanctioned platforms like Outlook e-mail and Microsoft Teams.

Canvas

We will be using Canvas, but only to submit assignments and to store all of the grades. I will also keep all of the due dates current on the calendar there, as students have appreciated this common space for all of their classes in the past.

Discord / Anonymous Questions

To facilitate informal, class-wide discussions about the class, as well as buddy group coding with screen sharing, we will have a Discord channel for the class. My goal is for this to turn into a flourishing area to work through confusion and to share ideas as a group.

Voltaire Anonymous Questions

All questions are welcome! To help break down the barrier of asking questions, we will be using the chat bot Voltaire so students can ask questions anonymously. This has worked very well in the past. To use Voltaire, send a direct message to the Voltaire bot with the following syntax:

where channel_name is the name of the channel you want to post to, and message is the text you want to send. Below is an example:

Answer:

In Class Anonymous Questions

In addition to Voltaire, I'll be running a bot during class that accepts questions to help quieter student who want to participate anonymously to do so. I've found this sort of thing to be particularly helpful with students from underrepresented groups. To ask a question during class, visit http://mathcs.ursinus.edu/question.

Other Rules

Do not send me direct messages or anything of a sensitive nature (e.g. grades) over Discord. Instead, use Microsoft Teams or e-mail for that, since those transactions are locked down better under Ursinus control.

Microsoft Teams

For one on one direct messages with me, and for buddy group coding with screen sharing, we will be using Microsoft Teams, which is linked to your Office suite through Ursinus, so you are automatically enrolled. This is an easy platform for students to initiate video sharing, so I highly recommend it for group work.

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Readings

There will be no official textbook for the course. Instead, I will be flipping about 2/3 of the classes with a number of modules that I made for you to complete before class.

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Deliverables

Programming Languages

There has been a long tradition of implementing multimedia algorithms in Matlab. The downside of Matlab is that it is proprietary and quite expensive, and aspects of the language beyond matrix multiplications are not very elegant. Thankfully, in recent years, a library in Python known as numpy has matured and allowed the Python language to accomplish everything that Matlab could with digital audio. So though the focus is more on the algorithms themselves, we will learn python and numpy as we go along in this class, and these are valued skills in data science well beyond audio processing.

Pre-Class Modules

Since the material from this class is so different from an ordinary CS class, I want students to have thought about it a bit before coming to class so we can answer deeper questions and do more interesting exercises. Therefore, I will be following a largely flipped model with 2-3 modules per week that walk you through concepts with videos I made, followed by autograded python/numpy exercises. I worked hard on these, so I hope you enjoy them, and I hope it's an opportunity for you to study at your own pace!

NOTE: These modules are meant to be low stakes compared to the assignments, so please reach out to me or post on discord to the rest of the class if you get stuck on anything. I'm looking forward to some lively discussions as students work through them!

Assignments

The bulk of the grade in the course will be earned by completing roughly 6 programming assignments. Be sure to start them early! Note that collaboration and sharing rules differ slightly between the individual assignments and the large assignments.

NOTE: I will not grade assignments that do not run! So be sure to hand in code that runs. Send me or the class messages ASAP when you run into syntax errors you can't resolve on your own.

Debugging Principles

If you're taking this course, then you've certainly had experience with debugging, but it is a skill you will still need to work on, so you should expect to hit some roadblocks. In fact, it is time consuming and difficult even for very experienced programmers. So do not be hard on yourself if your programs don't work the first time around (they rarely do, even if you've been programming for decades!).

I have had nearly 20 years of programming experience at this point, and I have learned the hard way what works and what doesn't. Here are my main debugging principles in a nutshell

1. Leave yourself adequate time to work on the assignments, because the amount of time it takes to resolve issues can be unpredictable.
2. Write small bits of code and test them right away. Don't write a wall of code and test it, only to find out that something doesn't work. By contrast, if you write bits at a time, you will know right away what code you wrote caused things to be wrong.
3. Apply the scientific method: have in mind hypothesis for what might be wrong, design a quick experiment to test your hypothesis, draw conclusions, and repeat.
4. Fail quickly. If you're working on a larger scale program that processes a lot of data, do not wait for several minutes for data to load every time you make a small change. Instead, come up with the minimum, simplest experiment you possibly can which will tell you whether your code is correct or not.
5. Don't forget that you can write code to help you automate debugging. Otherwise, it's sometimes tedious to repeat the same steps over and over again as you're changing things.
6. Know when to walk away. We often get stuck in loops wanting to resolve things, but then our logical thinking goes out the window and we start randomly trying different things. Even if you're up against the clock, it is often good to take a little break and come back again a little bit later.

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Schedule

Outlined below is the schedule for the course, including lecture topics and assignment due dates. All assignments are due at 11:59PM on the date specified. The specific dates of different topics are subject to change based on the pace at which we go through the course.

Lecture	Date	Lectures (click for notes)	Readings/Links	Assignments/Deliverables
Unit 1: Time Domain Audio Processing We will begin our foray into digital audio processing by thinking of audio as a big, long 1D array. Under typical conditions, we have 44100 elements for every second of audio. This means we have nearly a million elements in our arrays for only 20 seconds of audio! We'll also see that the elements of this array, known as "audio samples," are a bit of a mess, but we'll be able to do a surprising amount by manipulating the samples, and we'll be able to create some cool sounds by synthesizing samples from equations. Students will also get more comfortable with python and numpy in the stage of the course.
1	Wed 1/18/2023	Course Overview, Sneak Preview, Digital Audio Representations	Slides from today Audio Reverse Game
	Thu 1/19/2023			Survey and syllabus quiz due by 11:59PM
2	Fri 1/20/2023	Python/Numpy Basics, Slicing/Reversing Audio, Sinusoids/Pitch	Sheehy Ch. 2 for python intro	Module 1: Python Basics Due by 11:59 PM
3	Mon 1/23/2023	Square Waves, Triangle Waves, Sawtooth Waves, Noise, Beat Frequencies		Module 2: Sinusoids And Simple Numpy Tunes Due Before Class Assignment 1: Risset Beats Out
4	Wed 1/25/2023	Octaves, Harmonicity, Circle of Fifths / Pythagorean Tuning	Harmonicity Notes Wikipedia Notes on The Pythagorean Comma	Module 3: Standing Waves And Plucked String Synthesis Due Before Class
5	Fri 1/27/2023	Finish Pythagorean Tuning, Chirps, Vibrato, Frequency Sonification
6	Mon 1/30/2023	Zero Crossings, Consonant/Vowel Filtering, Dynamics, Intensity, Loudness		Module 4: Chirps, Instantaneous Frequency Due Before Class
7	Wed 2/1/2023	HW1: Risset Beats Q&A, Work Day	Happy Birthday Square Waves Phase Aggregation (Part 2) Notes
8	Fri 2/3/2023	Timbre, FM Synthesis, ADSR	John Chowning's 1973 paper on FM synthesis Yamaha DX7 Stanford CCRMA	Module 5: Zero Crossings Filtering, Loudness And Intensity / Dynamics Due Before Class Assignment 1 Due
9	Mon 2/6/2023	Echoes / Impulse Responses / Convolution	Echo Thief Impluse response of a nuclear power plant	Module 6: Timbre, FM Synthesis, Python Methods As Parameters due before class Assignment 2: Digital Instruments Out
10	Wed 2/8/2023	Comb Filters		Module 7: Echoes, Impulse Responses, And Convolution Due Before Class
Unit 2: Frequency Domain Audio Processing We are now ready to introduce one of the most important tools in this class: the Discrete Fourier Transform (DFT). As it turns out, every possible sound can be represented as a sum of sines and cosines at different frequencies! If we examine the amplitudes of these sines/cosines instead of the audio samples directly, this can give us a lot more insight into what's actually going on. This is because, as we observed last semester, we perceive anything "periodic" (that repeats itself in time) as a pitch, and sines/cosines are the fudamental building blocks of any periodic signal. Hence, the DFT describes audio with numbers that are much closer to what we perceive about the audio than the raw audio samples.
11	Fri 2/10/2023	Discovering The Discrete Fourier Transform (DFT)		Module 8: Discovering The Fourier Transform Due Before Class
12	Mon 2/13/2023	Real-Valued DFT Definition, Amplitude/Phase Shifts		Module 9: The Real Discrete Fourier Transform (DFT), Amplitude/Phase Due Before Class
13	Wed 2/15/2023	Exploring The DFT on Audio Data: Pitch And Timbre, Fast(er) Risset Beats
14	Fri 2/17/2023	Converting Frequency Units on Real Audio, Begin The Short-Time Fourier Transform (STFT)		Module 10: DFT on Real Audio, DFT on Sawtooth/Square Waves, Fundamental DFT Properties, Inverse DFT And Fast Risset Due Before Class
15	Mon 2/20/2023	The Short-Time Fourier Transform (STFT), Spectrograms	Live spectrogram demo in the browser
16	Wed 2/22/2023	Complex Numbers Review, Euler's Formula, Complex Definition of DFT And Phase	The Other Square Wave (i.e. how we don't pereceive constant phase offsets)	Module 11: STFT, Window Functions, Complex Numbers Due Before Class
	Thu 2/23/2023			Assignment 2 Due
17	Fri 2/24/2023	Phasor Mirroring, Aliasing, Inverse DFT		Module 12: Complex DFT And Phasors Due Before Class Assignment 3: Spectacular Spectrograms Out
18	Mon 2/27/2023	Inverse STFT, Vocoder Concept, Phase Retrieval		Module 13: Aliasing Due Before Class
19	Wed 3/1/2023	Convolution/multiplication duality
20	Fri 3/3/2023	The Z-Transform, Lowpass Filters / Highpass Filters, frequency analysis of comb filters	Ableton: Filters in The Real World	Module 14: Convolution And Multiplication Duality Due Before Class
Unit 3: Classical Machine Learning / AI For Digital Music Processing Now that we have frequency domain tools down, we're going to build things on top of it to address several higher level problems, including: Tempo estimation and beat tracking Timbre and pitch modeling for audio classification The "Shazam algorithm" for audio fingerprinting Hierarchical music structure estimation Cross-version comparison and time warping "Un-mixing" audio, or separating audio into its tracks All of these problems can be accomplished with straightforward mathematical algorithms built on top of the Short-Time Fourier Transform (STFT).
--	Mon 3/6/2023	Spring Break		No CS 372 Class. Enjoy the break!
--	Wed 3/8/2023	Spring Break		No CS 372 Class. Enjoy the break!
--	Fri 3/10/2023	Spring Break		No CS 372 Class. Enjoy the break!
21	Mon 3/13/2023	Rhythm, Beats, And Audio novelty functions
22	Wed 3/15/2023	Audio novelty functions And Autocorrelation		Module 16: Exercise 1 Audi Onsets/oFfsets Due Before Class
	Thu 3/16/2023			HW3 Due
23	Fri 3/17/2023	Matrix Multiplication, Mel Spacing, Dynamic Programming Beat Tracking	Notes on dynamic programming beat tracking	Module 16: Exercise 2 Autocorrelation and 3 Matrix Multiplication Due Before Class
	Sun 3/19/2023			HW4: Tempo Estimation And Beat Tracking Out
24	Mon 3/20/2023	MFCC Features, Inverting MFCC		Module 16: Exercise 4 Mel Spectrogram Due Before Class
25	Wed 3/22/2023	Chroma Features, Shepard Tones	Shepard Tone Visualization
26	Fri 3/24/2023	Audio Fingerprints / The Shazam Algorithm	The original Shazam Paper Our Python Implementation of Shazam Blair Kaneshiro Talk Cinco Midi Organiser
27	Mon 3/27/2023	Harmonic-Percussive Source Separation, Matrix Multiplication for Audio Activations	Class notes on HPSS FitzGerald 2010 Paper
28	Wed 3/29/2023	Source Separation via Nonnegative matrix factorization (NMF)	Class Notes on NMF Original Lee/Seung Paper on NMF	Module 18: Matrix Activations for Spectral Decompositions Due Before Class
29	Fri 3/31/2023	Intermezzo: Dynamic Programming Beat Tracking	Class notes on dynamic programming beat tracking
30	Mon 4/3/2023	Fundamental Frequency Estimation, Autotuners	Class notes on autotuners Video on autotuners Justin Salamon: Replace Your Favorite Singer With a Robot	HW4 Due HW5: Let It Bee Out
31	Wed 4/5/2023	Self-Similarity in Audio, Spectral Clustering	Foote 1999 Paper: Visualizing music and audio using self-similarity Arthur Lintgen bio Mcfee 2014 paper Tralie 2019 Paper Graph Ditty	Module 17: Sonifying Mel And Chroma Filterbanks Due Before Class
32	Fri 4/7/2023	Cross-Similarity Matrices, Beat-Synchronous Version Alignment	Cover Song Alignment Demo
33	Mon 4/10/2023	Dynamic Time Warping	CS 472 Module on Dynamic Time Warping CS 371 Notes on Dynamic Time Warping Linear memory dynamic time warping
Unit 4: Data Driven Digital Music Processing In the prior unit, we built on spectrograms to do a variety of tasks using hand-crafted models to pick up on different audio attributes. In this unit, we will talk about modern methods for data-driven audio analysis. Rather than going in with too many preconceived notions about what we need to do to pick up on particular audio attributes, we will work to collect labeled data that captures what we want the computer to be able to do, and then we will let the computer tell us what patterns exist in the data that allow us to do certain tasks. For instance, we will gather a large collection of notes being played on bass guitar, and we will learn how to synthesize bass notes from this data. We will also discuss how to do beat tracking by learning from a large collection of audio clips where the beats are annotated. In general, the approach of learning to generalize from labeled data examples is known as supervised learning. Though there are a variety of techniques to do this, we will focus primarily on deep neural networks, as they have shown the most promise for audio analysis and synthesis over the past few years. As such, this is the most "cutting edge" unit in the course.
34	Wed 4/12/2023	Logistic Regression, Introduction To pytorch and data loaders	PyTorch Datasets & DataLoaders Documentation	Module 20 Parts 1-3 Due Before Class
35	Fri 4/14/2023	Logistic Regression: Separating Square Waves from Sawtooth Waves		Rest of Module 20 Due Before Class
36	Mon 4/17/2023	Neural Networks And Deep Learning	3Blue1Brown Intro To Neural Nets	Module 21: Neural Networks/a> Due Before Class
--	Wed 4/19/2023	COSA Day		No CS 372 Class. Enjoy the break!
37	Fri 4/21/2023	Convolutional Neural Networks, Data Augmentation		Module 22: Multiclass Classification, Convolutional Neural Networks, And Overfitting Due Before Class HW5 Due
38	Mon 4/24/2023	Autoencoders Sneak Preview, Differentiable Digital Signal Processing	Google Magenta DDSP Blog Post
	Tue 4/25/2023			HW6: String Along Out
39	Wed 4/26/2023	2D Convolutional Networks: CREPE (Fundamental Frequency) And Deep beat tracking	Jong Wook Kimg 2018: CREPE: A Convolutional Representation for Pitch Estimation Schlüter/Böck 2014: "Improved Musical Onset Detection with Convolutional Neural Networks Davies/Böck 2019: Temporal Convolutional Neural Networks for Musical Audio Beat Tracking
40	Fri 4/28/2023	Autoencoders, LPC, Autoregressive Coding	Notes on Linear Predictive Coding DeepMind WaveNet blog post
41	Mon 5/1/2023	Wave-U-Net for Demixing	Wave-U-Net Paper Code from class
42	Wed 5/3/2023	Ethics Discussion: Music in The Era of Generative AI
	Thu 5/11/2023			HW6: String Along Due

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Grading

Breakdown

Class Engagement / Pre-Class Modules	25%
Programming Assignments	75%

Flexible Submission Policy

In the absence of accommodations or communication with me, all assignments are due at 11:59PM EST on the date(s) stated on the schedule. Students can turn in those assignments past the deadlines, and the scores will be adjusted as follows:

• -5% for work submitted between 1 minute - 6 hours late
• -10% for work submitted up to 12 hours late
• -15% for work submitted up to 24 hours late
• -25% for work submitted up to 48 hours late
• -40% for work submitted up to 96 hours late
• -50% for work submitted more than 96 hours late but before solutions are returned

Letter Grades

Letter grades will be assigned on the scale below at the end of the course.

A+: 99+ A: 93-98.9 A-: 89.5-92.9

B+: 87-89.49 B: 83-86.9 B-: 79.5-82.9

C+: 77-79.49 C: 73-76.9 C-: 69.5-72.9

D+: 67-69.49 D: 63-66.9 D-: 59.5-62.9

F: 59.49 and below

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Classroom Environment

Inclusive Environment

Computer science is a field that has historically been and continues to be steeped in inequalities. We have a double dose of this in digital music processing, since modern work in this area is adjacent to AI and machine learning, which themselves have added fuel to the fire.

Within class interactions, my goal is to foster a environment in which students across all axes of diversity feel welcome and valued, both by me and by their peers. Axes of diversity include, but are not limited to, age, background, beliefs, race, ethnicity, gender/gender identity/gender expression (feel free to tell me in person or over e-mail which pronouns I should use), national origin, religious affiliation, and sexual orientation. Discrimination of any form will not be tolerated.

A slightly more subtle thing that we want to watch out for in class is not to assume anything about others based on implicit biases. I have heard from quieter students at Ursinus from different backgrounds in the past that they sometimes feel their contributions aren't valued from their group members and that they're talked over during collaborations. So be mindful of this and be sure to curb this behavior in yourself if you notice it...this is something immediate you can to do help stem the leaky pipeline in our field.

.Furthermore, I want all students to feel comfortable expressing their opinions or confusion at any point in the course, as long as they do so respectfully. As I will stress over and over, being confused is an important part of the process of learning computer science. Learning computer science and struggling to grow is not always comfortable, but I want it to feel safe. In other words, I will regularly keep you at the boundary of your comfort zone with challenging, real-world assignments, but I want you to feel comfortable with me and your peers and respected as a learner during the process.

Finally, I am aware that, particularly during the pandemic, there are a variety of factors that may make it difficult to perform at your best level in class. At Ursinus, we are fortunate to have quite a mix of students from different backgrounds, many of whom need to work part time, and an increasing number of whom are commuters and have family obligations. If you find yourself having difficulty performing at the level that you want and/or turning assignments in on time because of any of these issues, communicate with me, and we can come up with a solution together (I will gently reach out if I notice any slips even if you don't communicate). This is an exciting elective course, and I want to work to keep your excitement alive, regardless of your personal circumstances. We will get you through the course as long as the lines of communication are open and we work together. You belong in CS!

Classroom Attendance And Etiquette

Students are expected to attend class in person. We're shooting for engagement over mere attendance; students are expected to be active in class exercises and to be fully invested in the class (i.e. no internet browsing). Students who are unable to attend class for significant reasons (whether isolation or quarantine for students who have received a positive test, those experiencing Covid-related symptoms while awaiting test results, or other issues that make it difficult to attend class) should work pro-actively to make up any class exercises that they missed. To help with this, I will do my best to put up Youtube videos from other instructors on topics that we cover.

Finally, students are expected to follow any college policy requiring mask wearing on campus, in addition to following any guidance faculty provide for their individual classes.Masks should be available in every academic building, if needed.

Maximizing Your Communal Experience

Here are ways students can maximize their experience as a class community, and which could lead to extra credit in certain situations.

• Helping to teach a student a topic during student office hours.
• Certain calls for participation in class
• Particularly helpful or insightful messages on Discord
• Finding mistakes in class notes or on the assigned homework

Discord Communication Policy

Since this is a class-wide communication, the following rules apply to online communication

• Students are expected to be respectful and mindful of the classroom environment and inclusivity standards. They are equally applicable to a virtual environment as they are in class.
• Students are not permitted to publicly share direct answers or questions which might completely give away answers to any homework problems. When in doubt, send me a direct message.
• I will answer questions real time during any office hours that are virtual. Otherwise, I will make every attempt to respond within 24 hours on weekdays. I cannot be expected to respond at all on Saturdays or Sundays or outside of 10AM-8PM on weekdays, so plan accordingly. (Of course, students can and should still respond to each other outside of these intervals, when appropriate).

The points above are part of a more general term referred to as "netiquette." Refer to the chart below, provided by Touro College

Collaboration Policy

Overall Philosophy

The collaboration policy for this class walks the line between encouraging openness and collaboration during a challenging learning process, while also making sure that each students is progressing technically at an individual level without relying on 100% on other classmates. Communication between students is allowed (and encouraged!) on most assignments, but it is expected that every student's code or writeups will be completely distinct. Do not copy code off of the Internet. Cite any sources in addition to materials linked from the course website that you used to help in crafting your code and completing the assignment.

Assignment Buddies

To encourage collaboration, students will be allowed (not required) to choose one or more "buddies" to work "near" during the programming assignments. Students are still expected to submit their own solutions, but they are allowed to provide substantial help to each other, and even to look at each others' code during the process. Students should indicate their buddies in the README upon assignment submission. Let me know if you would like a buddy but are having trouble finding one.

Collaboration Scenarios Table

Below is a table spelling out in more detail when and how you are allowed to share code with people (table style cribbed from Princeton CS 126).

Click on each button below to view the collaboration parameters for individual and budd (group) assignments

Assignment BuddyIndividual

* You may view and copy code from class exercises and class resources without citing them, but you should not copy solutions from previous semesters that the instructor may have provided

NOTE: The terms "exposing" and "viewing" exclude sending or ingesting electronically, which would be considered copying. Exposing and viewing are normally done in the context of in-person working or in the help room. When students work remotely, what this means is that buddies can screen share as they are working through things, but they should not send code directly.

NOTE ALSO: "Other people" includes internet sources.

If the collaboration policy has been violated in any way, regardless of intent, then it may be an academic dishonesty case, and it will be referred to the Associate Dean for Academic Affairs. I am required to make this report in every occurrence, so it is best to speak with me first if there are any questions about the policy or expectations. You should feel free to have these conversations with me anytime prior to making your submission without fear of penalty.

On a more personal note, though a willful violation of academic honesty may seem merely transactional to a student, faculty take violations very personally, as they are disrespectful to the time and effort we put into our courses. I would also like to emphasize that your reputation is much more important than your grades. The recommendations we as faculty write go a long way, and we are much happier to write positive recommendations for students with lower grades who show grit and growth than we are to write recommendations for students with higher grades who have cheated.

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Other Resources / Policies

Accommodations

In addition to our general awareness diversity, Ursinus College is also committed to providing reasonable accommodations to students with disabilities. Students with a disability should contact the Directory of Disability Services ASAP. Dolly Singley is located in the Center for Academic Support in the lower level of Myrin Library. Visit this link for more information on the process. I will do my best to accommodate your requests, and they will be kept completely confidential.

One on One Tutoring

One on one tutoring for up to two hours per week is available through the institute for student success. Click here to fill out a Qualtrics survey if you'd like to take advantage of this.

Let's Talk

Mental health care is increasingly recognized as a crucial service for the undergraduate population. Visit this link for more information about complementary counseling services provided by the college. The Wellness Center has a virtual drop-in crisis hour at 2-3 pm each weekday, which is available for students in crisis who need to be seen immediately by a clinician. If you are still hesitant to go, take me (Professor Tralie) as an example of someone who has benefited greatly from talk therapy and medication in the past. I am happy to discuss this in student office hours in more detail.

Beyond that, please have a look at this document for a variety of resources related to mental health at Ursinus.

Finally, be aware that there are resources outside of mental health care proper to address some core sources of mental stress and strain, such as time management and writing at the Institute for Student Success and the Center for Writing And Speaking.

Title IX

Title IX is a federal law, under which it is prohibited to discriminate on the basis of gender. The Title IX Coordinator is available to receive inquiries and to investigate allegations in this regard.

Inclement Weather Policy (aka COVID Policy)

In the event that the College closes due to inclement weather or other circumstances (such as a COVID lockdown), our in-person class sessions, drop-in student office hours, or other meetings will not be held. I will contact you regarding our plan with regard to rescheduling the class or the material, any assignments that are outstanding, and how we can move forward with the material (for example, any readings or remote discussions that we can apply). If necessary, I may schedule online virtual sessions in lieu of class sessions, and will contact you with information about how to participate in those. I will communicate this plan to the department so that it can be posted on my office door if it is feasible to do so. This policy and procedure will also apply in the event that the College remains open but travel conditions are hazardous or not otherwise conducive to holding class as normal. Should another exigent circumstance arise (for example, illness), I will follow this policy and procedure as well.