UNIVERSITY OF MIAMI FROST SCHOOL OF MUSIC
A METRIC FOR MUSIC SIMILARITY DERIVED FROM PSYCHOACOUSTIC FEATURES IN DIGITAL MUSIC SIGNALS
By Kurt Jacobson A Research Project
Submitted to the Faculty of the University of Miami in partial fulfillment of the requirements for the degree of Master of Science in Music Engineering Technology
Coral Gables, FL April 2006
UNIVERSITY OF MIAMI
Submitted in partial fulfillment of the requirements for the degree of Master of Science in Music Engineering Technology
A METRIC FOR MUSIC SIMILARITY DERIVED FROM PSYCOACOUSTIC FEATURES IN DIGITAL MUSIC SIGNALS
Kurt Jacobson
Approved:
_________________________________ Ken C. Pohlmann Professor, Music Engineering
_________________________________ Dr. Edward P. Asmus Associate Dean, Graduate Studies
_________________________________ _________________________________ Fred DeSena Dr. James Shelley Asst. Prof., Music Theory and Composition VP, Academic and Research Systems
JACOBSON, KURT
(M.S. in Music Engineering Technology) (April 2006)
A Metric for Music Similarity Derived from Psychoacoustic Features In Digital Music Signals Abstract of a Master's Research Project at the University of Miami Research project supervised by Professor Ken Pohlmann From purchase to playback, digital music formats are becoming the pervasive mode of music consumption. Technologies like perceptual audio encoding and peer-topeer networking have enabled even casual enthusiasts to amass large digital music collections. New online music services offer customers millions of song titles for download. Portable digital music players allow listeners to carry thousands of music files in their front pocket. At every level, the amount digital of music content available for consumption is growing to nearly unmanageable proportions. Finding better ways to organize, index, and search digital music collections is the focus of content-based music information retrieval (MIR). A diverse body of research, MIR deals with problems like automatic genre classification, automatic song summarization, and music similarity quantification as well as others. This work describes a system for deriving music similarity measures from a set of music signals using digital signal processing techniques. The system employs three distinct dimensions of similarity: timbral similarity, rhythmic similarity, and structural similarity, to place individual songs in a "music similarity space." The system is tested on a set of popular music files obtained from the iTunes online music store as well as other music collections. Multidimensional scaling of the resulting similarity data is used to visualize song files in the music similarity and calibrate the system to estimate genre boundaries. Also, an intelligent jukebox application is implemented that generates playlists based on the system's music similarity measures.

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