CLASS LOCATION: Tech MG 28
DAYS AND HOURS: Mon, Wed 2:00pm - 3:20pm

INSTRUCTOR: Bryan Pardo
office location: 3-323 Ford Building
office phone number: 847 491 7184
office hours: 3:30 - 4:30pm, Monday

REQUIRED TEXTBOOK: Signals Sound and Sensation by William M. Hartmann

ACCESS TO MATLAB:

Lab assignments will be done in Matlab. Matlab is available in the T-lab, located at the north end of the connector between Tech and the Ford building. All of the T-lab boxes are dual-boot Linux/Windows. If all of the machines that default to Linux are in use, a student simply needs to reboot a Windows box. Right after the BIOS, the GRUB bootloader will prompt for the OS to boot. Matlab can then be started buy typing the following command at the command prompt:

/usr/local/matlab/bin/matlab &

The EECS tech support people will use the class list and work with the lock shop to give access to the T-Lab to enrolled students in the class. Christopher Bachmann will also create accounts for students who do not already have one. Those students will receive an email with instructions to access their account. There may be some students who do not remember their password or where it has expired. If that is the case, then they should email root@eecs.northwestern.edu. Additionally, any students who find their WildCard does not unlock the door should stop by the EECS tech support office in Tech M334 so that we may update their access.

PREREQUISITES:

Prior programming experience sufficient to be able to do laboratory assignments in MATLAB is required. Completion of the Engineering Analysis (GEN_ENG 205-2) series or EECS 211 or EECS 231 would demonstrate sufficient experience. A willingness to deal with math is also a prerequisite.

COURSE GOALS:

How do you tell the sound of a clarinet from the sound of a kazoo? Is this song a waltz or a tango? If your friend likes Yo La Tengo, would she prefer a CD by the Flaming Lips or Bon Jovi? Can a computer answer these questions?

Researchers in computational music perception apply signal processing, psychology, music theory, machine learning, and natural language processing techniques to auditory user interfaces for human-computer interaction. Current application areas include vocal interfaces and search engines for music databases, machine accompaniment of human musicians, automated music recommendation systems, and tools for music production.

Machine Perception of Music will introduce students to the field of computational music perception through a combination of lectures, readings, and lab work in MATLAB. Students will learn basics of how sound and music are recorded and encoded by computers as .wav and MIDI files. The class will also explore basics of audio perception, including the relationship between pitch and frequency and the difficulties inherent in auditory scene analysis by humans and machines. Basic classification and sequence alignment techniques will also be introduced.