College application research evidence · understanding and original contribution · Seoul and online
Project courseThe student keeps the work and the record they made.
AI + Music: Affective Computing Research
Grades 9-12: expressed versus felt emotion in music...
Where this course comes fromThe universities and labs named here made the open projects or materials this course draws on. CIT designed the course independently; it is not an official, affiliated, or endorsed course of those institutions.
- University of GenevaOpen research referenced
- Free consultation and level check
- Apgujeong classroom or online
- One-to-one or small group
- Registered academy no. 11882
Is AI + Music: Affective Computing Research a good fit for high school students in Grades 9-12?
AI + Music: Affective Computing Research is a good fit for students in grades 9-12 who are interested in expressed versus felt emotion in music and music dataset licensing. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a paper-style report with a reproduction package, a dataset comparison table and model card, and a research notebook or adaptive music prototype.
The course was designed from open research published by University of Geneva. Students separate the emotion a piece of music expresses from what a listener feels, pick one as the research target, and rule out any claim that AI can read a person's inner emotions. They compare the labels, splits, and licenses of DEAM, AudioSet, and MAESTRO, build baselines and models from mel audio features and MIDI tempo and velocity, and measure how much scores rise when the same song leaks into both training and testing. With errors analyzed by genre, instrument, and recording quality, the work ends as a paper-style report or a user-controlled adaptive music system, packaged so others can reproduce it. The student then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
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I appreciated that a simple early idea was extended into an 'AI debate tool'. My child has always been interested in debate and social issues, so seeing that interest connect to the project makes me expect a more complete and distinctive result.
- Age group
- Grades 9-12
- Academic subject
- AI & Data, Arts, Media & Design, Research & PortfolioBrowse subject
- Course type
- Course
- Format
- Online or Apgujeong in person · one-to-one or small group
- Teaching language
- Korean by default, with complete English materials
- Curriculum status
- Lesson-readyEvery learning item includes the evidence, worked example, practical work, and tutor guidance required by CIT's lesson-ready standard.
From open research to student-owned work
Students inspect a relevant public source, reproduce the idea, then add an original question, feature, or test. The material this course reads was made at University of Geneva. Names identify the source, not affiliation or endorsement.
- University of GenevaOpen research referenced
How can I explain this course to my child?
If questions about expressed versus felt emotion in music or music dataset licensing keep making you ask why, AI + Music: Affective Computing Research lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- expressed versus felt emotion in music
- music dataset licensing
- cultural bias in emotion models
What does the student finish?A paper-style report with a reproduction package, a dataset comparison table and model card, and a research notebook or adaptive music prototype.
Is this a good fit?
A strong fit for students who want to understand, build, test, or responsibly use AI systems.
Course placement follows current subject and coding readiness.
Students should be ready to document sources, methods, and limits.
Advanced tools are introduced after a clear baseline.
What will my child learn?
- Distinguish expressed, perceived, and felt emotion, and define one of them as the construct under study.
- Trace how sample rate, frames, windows, spectra, and mel features transform an audio signal, and fix every setting.
- Quantify leakage by comparing random segment splits with splits grouped by song and artist.
- Report prediction spread, calibration, and refusal zones, and limit the model's claimed scope using results by genre and recording condition.
- University of GenevaOpen research referenced
How does the course progress?
- 1Music Data, Rights, and Measurement
Define the emotion construct, compare licenses and access terms for three datasets, and build a fixed-setting audio feature pipeline.
- 2Modeling Emotional Expression
Analyze MIDI tempo and velocity features and the spread of valence-arousal labels, then check linear and logistic baselines with cross-validation.
- 3Leakage, Bias, and Uncertainty
Measure leakage with song- and artist-grouped splits, and record calibration, refusal rules, and genre bias in a model card.
- 4Reproducible Music Research
Finish a user-controlled adaptive system with set input priorities, or a research notebook, and close with a report on a failure with an unseen clip.
What counts as useful evidence?
A paper-style report with a reproduction package, a dataset comparison table and model card, and a research notebook or adaptive music prototype.
What should an admissions reader be able to see?
The course was designed from open research published by University of Geneva. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits.
Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
These names identify who published the open project the course starts from. The evidence an application reads is what the student understood, completed, and can explain.
Which sources were reviewed?
Catalog reviewed: 2026-09-30
- University of GenevaOpen research referenced
Questions parents search before choosing this course
Is AI + Music: Affective Computing Research a good fit for high school students in Grades 9-12?
AI + Music: Affective Computing Research is a good fit for students in grades 9-12 who are interested in expressed versus felt emotion in music and music dataset licensing. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make a paper-style report with a reproduction package, a dataset comparison table and model card, and a research notebook or adaptive music prototype.
Can my child take AI + Music: Affective Computing Research online or in person, one-to-one or in a small group?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A readiness consultation confirms the available format and starting point for the course.
Does my child need prior subject knowledge or coding experience for AI + Music: Affective Computing Research?
Course placement follows current subject and coding readiness. Students should be ready to document sources, methods, and limits. Advanced tools are introduced after a clear baseline.
What will my child make or practice in AI + Music: Affective Computing Research?
The main evidence is a paper-style report with a reproduction package, a dataset comparison table and model card, and a research notebook or adaptive music prototype. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can AI + Music: Affective Computing Research show research understanding in a college application?
The course was designed from open research published by University of Geneva. The student works out what those researchers were trying to learn, why the question matters, and how they tested it, then designs and tests an extension of their own: a new question, feature, model, interface, or solution. In an application or interview, the student separates the source research from their own decisions, results, failed attempts, revisions, and limits. Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
How are the schedule and tuition for AI + Music: Affective Computing Research determined?
CIT confirms the student's readiness, goal, location, class size, and current availability before recommending a course plan. The consultation and level check are free; tuition is explained before enrollment.
When should a student start?
There is no fixed intake month. CIT reviews the student's current school term, readiness, and available hours, then names the point in the course where they should begin.
The teachers are always attentive and considerate, so we feel comfortable trusting them with our child.
Where else parents check CIT
Choose the course after a readiness check
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