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Donald S. Williamson
Advisor: Youngmoo Kim, Ph.D.
Abstract:
The growth of digital music has caused a new set of problems pertaining to music recommendation and organization. Mp3 players such as iPods have exacerbated the task of organizing music due to the exorbitant amount of songs that a single device may contain. iPods carry thousands of songs, making it extremely tedious and time- consuming to perform the simple task of playlist generation. Most users simply place their iPods on shuffle mode, allowing the device to randomly determine which songs are played. This random mode of listening is not what we usually desire, since it doesn’t give the listener the opportunity to select a particular style or kind of music.
This project explores a method that uses a computer algorithm to assess song similarity, where the degree of similarity is based solely on the sound waveform of various songs. By extracting acoustic features that represent the timbre, or sound quality, of a song similarity is assessed by comparing the quantitative distance between features. The similarity between songs is also visually revealed through a computer interface, where songs that are similar are clustered together and dissimilar sounded songs are placed further apart. This visualization can serve as a means for recommending music based on similarities. Human subjects have also rated the similarity between songs by using a predefined rating system. A final evaluation of this algorithm consists of comparing the data generated from human subjects to the data generated by the automatic song similarity algorithm.
Tuesday, June 12th, 2007 at 5 p.m.
Bossone 303
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