Automatically Discovering Talented Musicians with Acoustic Analysis of YouTube Videos
Venue
Proceedings of the 2012 IEEE 12th International Conference on Data Mining (ICDM), IEEE Computer Society, Washington, DC, USA, pp. 559-565
Publication Year
2012
Authors
Eric Nichols, Charles DuHadway, Hrishikesh Aradhye, Richard F. Lyon
BibTeX
Abstract
Online video presents a great opportunity for up-and-coming singers and artists to
be visible to a worldwide audience. However, the sheer quantity of video makes it
difficult to discover promising musicians. We present a novel algorithm to
automatically identify talented musicians using machine learning and acoustic
analysis on a large set of "home singing" videos. We describe how candidate
musician videos are identified and ranked by singing quality. To this end, we
present new audio features specifically designed to directly capture singing
quality. We evaluate these vis-a-vis a large set of generic audio features and
demonstrate that the proposed features have good predictive performance. We also
show that this algorithm performs well when videos are normalized for production
quality.
