By Topic

Note onset detection for the transcription of polyphonic piano music

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
vd Boogaart, C.G. ; Multimedia Comput. Lab., Univ. of Augsburg, Augsburg, Germany ; Lienhart, R.

Transcription of music is the process of generating a symbolic representation such as a score sheet or a MIDI file from an audio recording of a piece of music. A statistical machine learning approach for detecting note onsets in polyphonic piano music is presented. An area from the spectrogram of the sound is concatenated into one feature vector. A cascade of boosted classifiers is used for dimensionality reduction and classification in an one-versus-all manner. The presented system achieves an accuracy of 87.4% in onset detection outperforming the best comparison system by 25.1 %.

Published in:

Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on

Date of Conference:

June 28 2009-July 3 2009