Ursinus CS 372: Digital Music Processing, Spring 2023
Week 11: Fundamental Frequency Tracking And Autotuners
Chris Tralie
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General
Overview
Technology Logistics
Homework
Grading
Classroom Environment
Participation
Collaboration Policy
Other Resources / Policies
Software
Piano Roll Editor
Schedule
Assignments
HW1: Risset Beats
Musical Statements
HW2: Digital Instruments
Musical Statements
HW3: Spectacular Spectrograms
Musical Statements
HW4: Tempo Estimation And Beat Tracking
HW5: Let It Bee
Musical Statements
HW6: String Along
Musical Statements
Class Exercises
Week 1: Audio Reverse Game
Week 2: Beat Phase
Week 2: Harmonicity
Week 3: Zero Crossings And Loudness Perception
Week 3: Harmonics And Timbre
Week 4: Timbral Envelopes
Week 4: Comb Filters
Week 4: The Discrete Fourier Transform
Week 6: 2D Arrays And Spectrograms
Week 6: Complex DFT
Week 7: STFT Noise Shaping
Week 8: Audio Novelty Functions
Week 9: Notes on Dynamic Programming Beat Tracking
Week 9: Mel-Frequency Cepstral Coefficients (MFCCs)
Week 9: Python Implementation of Shazam
Week 10: Harmonic/Percussive Source Separation with Median Filters
Week 10: Nonnegative Matrix Factorization for Demixing
Week 11: Fundamental Frequency Tracking And Autotuners
Week 12: Linear Separability of Phase-Shifted Triangle/Square Waves
Week 15: Wave-U-Net
Pre-Class Modules
Module 1: Digital Audio Waveforms, Python Basics
Module 2: Sinusoids And Simple Numpy Tunes
Module 3: Standing Waves And Plucked String Synthesis
Module 4: Chirps, Instantaneous Frequency, Vibrato, Sonification
Module 5: Zero Crossings Filtering, Loudness And Intensity / Dynamics
Module 6: Timbre, FM Synthesis, Python Methods As Parameters
Module 7: Echoes, Impulse Responses, And Convolution
Module 8: Discovering The Discrete Fourier Transform
Module 9: The Real Discrete Fourier Transform (DFT), Amplitude/Phase
Module 10: DFT on Real Audio, DFT on Sawtooth/Square Waves, Fundamental DFT Properties, Inverse DFT And Fast Risset
Module 11: STFT, Window Functions, Complex Numbers
Module 12: Complex DFT And Phasors
Module 13: Aliasing, Inverse DFT
Module 14: Convolution And Multiplication Duality
Module 15: The Z Transform
Module 16: Audio Novelty Functions, Tempo Estimation, Matrix Multiplication
Module 17: Sonifying Mel And Chroma Filterbanks
Module 18: Matrix Multiplication for Audio Activations
Module 19: Self-Similarity Matrices
Module 20: Intro To Supervised Learning, Logistic Regression, Gradient Descent, And PyTorch
Module 21: Neural Networks
Module 22: Multiclass Classification, Convolutional Neural Networks, And Overfitting
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