You are currently logged in as an
Institutional Subscriber.
If you would like to logout,
please click on the button below.
Home / Publications / E-library page
Only AES members and Institutional Journal Subscribers can download
A system for the automatic determination of symbolic drum patterns along with the downbeat is presented. From an unlabeled database of over 20000 urban music songs, for each song a characteristic drum pattern of one measure length is extracted fully automatically. The 50 most frequently occurring patterns are identified. For each of the most frequently occurring patterns the downbeat is determined by investigating the cue of the drum track. An evaluation against ground truth annotations for the drum patterns is carried out, where an accuracy of 90% for the downbeat detection is achieved. Further, a listening test has been carried out, that verifies the ground truth annotations.
Author (s): Gärtner, Daniel
Affiliation:
Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany
(See document for exact affiliation information.)
Publication Date:
2014-01-06
Import into BibTeX
Session subject:
Machine Learning Methods for Audio Content Analysis
Permalink: https://aes2.org/publications/elibrary-page/?id=17108
(313KB)
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
Gärtner, Daniel; 2014; Unsupervised Learning of the Downbeat in Drum Patterns [PDF]; Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany; Paper 3-3; Available from: https://aes2.org/publications/elibrary-page/?id=17108
Gärtner, Daniel; Unsupervised Learning of the Downbeat in Drum Patterns [PDF]; Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany; Paper 3-3; 2014 Available: https://aes2.org/publications/elibrary-page/?id=17108