Cours après Cours (Daily Notes)

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Échéances et Traveaux
Deadlines and Assignments
Introduction: Signal Processing, Machine Learning, and Python
Jan 09, 12 Some helpful links for starting off : Optional reading for those who don't know a lot about signal processing:
Optional reading for those who don't know a lot about machine learing:
  • The first four chapters of Pattern Recognition and Machine Learning (Christopher Bishop)
Mon Jan 12: HW 1 posted
Sparse Encoding of Audio
Jan 14, 19, 21 Papers:
Tutorials (Machine Learning/SVM) from Mon, Jan 19:
  • Doug's regression tutorial: regression.pdf
  • Doug's classification tutorial: classification.pdf
  • SVM Powerpoint tutorial by Mingyue Tan : svm_tutorial.ppt (the one I did in class was different. I was asked by it's author not to distribute it on the class website. If you would like it, please contact me by email.)
Other papers to read: Supporting material:
Classification and Similarity
Jan 26,28; Feb 2,4 Papers:
Other paper to read: Supporting Material:
Mon Feb 2: HW 1 due
Tempo, Beat and Meter
Feb 9,11 / Feb 16,18 / Feb 23 Papers:
Other papers to read
Supporting Material:
Mon Feb 16: HW 2 posted
Expressive Performance
Feb 24, Mar 9,11,16  
Blind Source Separation
Mar 16 Mon March 16 HW 2 due
Polyphonic Pitch Estimation
Mar 18, 23 Other papers to read: Wed Mar 18: Project proposal. An email with
  1. clear proposal
  2. citations
  3. proposed algorithm
  4. evaluation method
  5. which part you propose doing for HW3
Sequence Generation
Mar 25  
Visualization (plus Pitch Tracking moved from previous week)
Mar 30 / April 1 Wed April 1: Homework 3 (A portion of your final project or a monophonic pitch tracker)
April 6, 8 Instructions for final project
Wed Apr 15 Last class: what we did not cover. Doug will present.  
Final Project
Tuesday 21 april 13h30 to 15h30 Corey, Jordan, Philippe, Pierre-Paul, Arnaud  
Monday 27 april 13h30 to 15h30 Mansoor,Jean-François, Thomas, Nicolas, Xavier  
Monday 1 may 23h59:59 Final deadline for projects!  

Old papers and demos from previous versions of the course


[1] What is "SEGDSP"? There is a decent free online textbook by Stephen Smith called the The Scientist and Engineer's Guide to Digital Signal Processing. It can be found at Local copies of the chapters are referred to here as "SEGDSP" and are found in the directory segdsp. For signal processing, I will refer to chapters in this textbook. However, since the concepts we're dealing with are basic (Fourier analysis, convolution, filtering...) feel free to use any DSP textbook you'd like.