Skip to content. Skip to navigation
McGill Home SOCS Home
Personal tools
You are here: Home Announcements and Events Seminars profile

Seminars
Fall 2013 Schedule
Winter 2014 Schedule
Archive


         
     
 
2013/11/13, MC103, 12 - 12:30

Music Self-similarity and complexity leveraged for composer classification and computational Turing tests
Ladan Mahabadi , McGill SOCS

Abstract:

Music is an information-bearing medium, containing progressions of notes and silences (musical events) ordered in time. Its complexity derives from its information-rich structure, which enables music to be molded to the imagination of any creator, regardless of time or geography. We use the prevalence of patterns and complex structures in music to (1) investigate self-similarity features extracted from musical rhythm, and (2) to create a more secure, user-friendly computational Turing test.

(1) We use similarities hidden in different layers of musical rhythm to construct a concise structural identity (temporal fractal features). This work extends the temporal self-similarity analysis to non-Western music and presents the fractal features in rhythm as a universal feature set. These features are used for longitudinal analysis of compositions in a composer's body of work, and are applied as composer descriptors for classification in two large Western and non-Western music score repertoires. I will present both Western and non-Western composer classification results, which show that fractal features provide complimentary information about the underlying structure that can be used to improve the accuracy of existing classifiers.

(2) The ubiquity of music across all cultures, its complex structure at different layers, and the perceptual characteristics and limitations of the human auditory system are leveraged to construct more accessible, aesthetically pleasing and secure computational Turing tests. Music-based CAPTCHAs, called mCaptchas, are introduced to improve Web accessibility for individuals with visual impairments, to help address and avoid susceptibility to security flaws of existing audio CAPTCHAs, and to improve the overall user experience. I will present empirical evidence of the scheme's security for over 2000 mCaptchas, while its usability is tested by approximately 500 individuals on the Amazon Mechanical Turk (AMT) online market. These results demonstrate that humans can efficiently and accurately solve the generated music-based challenges while sophisticated computer programs fail.