CS 352 MACHINE PERCEPTION OF MUSIC AND AUDIO
Northwestern University Winter 2021
This course covers machine extraction of structure in audio files covering areas such as source separation (unmixing audio recordings into individual component sounds), sound object recognition (labeling sounds), melody tracking, beat tracking, and perceptual mapping of audio to machine-quantifiable measures.
This course is approved for the Breadth Interfaces & project requirement in the CS curriculum.
Prior programming experience sufficient to be able to do laboratory assignments in PYTHON, implementing algorithms and using libraries without being taught to do so (there is no language instruction on Python). Having taken EECS 211 and 214 would demonstrate this experience.
Time & Place
Lecture: Tuesday, Thursday, 6:30 - 7:50pm CST on ZOOM
Prof. Bryan Pardo Office Hours & Location:
Mondays 5:00 - 6:30pm CST on ZOOM
Questions outside of class
Please use CampusWire for class-related questions.
You will be graded on a 100 point scale (e.g. 93 to 100 = A, 90-92 = A-, 87-89 = B+, 83-86 = B, 80-82 = B-…and so on).
Homework and reading assignments are solo assignments and must be original work.
Final projects are group assignments and all members of a group will share a grade for all parts of the assignment.
Assignments must be submitted on the due date by the time specified on Canvas. If you are worried you can’t finish on time, upload a safety submission an hour early with what you have. I will grade the most recent item submitted before the deadline. Late submissions will not be graded.
Students can earn a MAXIMUM TOTAL of 10 extra-credit points (A full letter grade):
Participation during lecture You will be asked to select 2 lectures for which you will be on-call. In your on-call lectures, I will feel free to call on you and will expect that you’ve done the relevant reading prior to lecture and will be able to engage in meaningful interaction on the lecture topic. Each on-call day will be worth 3 points, for a total of 6 class participation points.
Paper reviews You will be able to earn extra credit by submitting reviews of up to 4 extra-credit papers in the field. Each paper review will be worth 1 point, for a total of 4 paper review points.
|1||Tue Jan 12||Course intro, Recording basics|
|1||Thu Jan 14||How we hear, Frequency & Pitch|
|2||Tue Jan 19||Loudness & Amplitude|
|2||Thu Jan 21||The Fourier Series & Spectrogram|
|3||Tue Jan 26||The Fourier Series & Spectrogram|
|3||Thu Jan 28||Convolution, Repetition||HW 1||20|
|4||Tue Feb 2||Filters, Reverb|
|4||Thu Feb 4||TBD|
|5||Tue Feb 9||Time-frequency masking & MP3||HW 2||20|
|5||Thu Feb 11||TBD|
|6||Tue Feb 16||Audio Source Separation|
|6||Thu Feb 18||Audio Source Separation|
|7||Tue Feb 23||Labeling Sound Events||HW 3||20|
|7||Thu Feb 25||Labeling Sound Events|
|8||Tue Mar 2||Music Similarity|
|8||Thu Mar 4||Music Similarity|
|9||Tue Mar 9||Deep Models for Audio||HW 4||20|
|9||Thu Mar 11||Deep Models for Audio||Xtra Credit||4|
|10||Thu Mar 18||Final assignment due||HW 5||20|
EXTRA CREDIT READING (Note 1/12/21: These will be updated. Don’t read yet!):
Places to get ideas
Essentia: an open source music analysis toolkit includes a bunch of feature extractors and pre-trained models for extracting e.g. beats per minute, mood, genre, etc.