Course Details

Music 8824: Computational Musicology II
Spring 2015
Tuesdays and Thurdays 2:20–3:40 – Room 503, Mershon Auditorium
Course description: The use of computers in music research and instruction; music data structures and programming routines for music research and instruction.

Course goals:
(1) to gain familiarity with signal processing methods and their application to audio and other musical signals
(2) to gain an understanding of how data extracted from audio signals relates to the psycho-physiological experience of hearing
(3) to gain experience with modeling data extracted with signal processing methods

Topics covered:
– Fundamental acoustic concepts concepts
– Software: Audacity, Sonic Visualiser, MATLAB
– Mathematical concepts used in signal processing
– Frequency domain transforms
– Low-level audio features: Fundamental frequency estimation
– Low-level audio features: Onset detection
– Low-level audio features: Loudness estimation
– Summarizing low-level audio features
– Applying signal processing to symbolic data
– High-level audio features: Timbre and beat/meter tracking
– High-level audio features:  Harmony and form/structure
– Machine learning: overview and clustering
– Machine learning: classification and temporal models

We will be using several pieces of software this term, as well as some code developed by the instructor. You can access both via this page.

Grade Breakdown
Weekly assignments 50%
In-class presentation 10%
Annotated bibliography 15%
Final project 25%