Please use this identifier to cite or link to this item:
|Title:||Multiple-feature fusion based onset detection for solo singing voice|
|Citation:||Toh, C.C.,Zhang, B.,Wang, Y. (2008). Multiple-feature fusion based onset detection for solo singing voice. ISMIR 2008 - 9th International Conference on Music Information Retrieval : 515-520. ScholarBank@NUS Repository.|
|Abstract:||Onset detection is a challenging problem in automatic singing transcription. In this paper, we address singing onset detection with three main contributions. First, we outline the nature of a singing voice and present a new singing onset detection approach based on supervised machine learning. In this approach, two Gaussian Mixture Models (GMMs) are used to classify audio features of onset frames and non-onset frames. Second, existing audio features are thoroughly evaluated for this approach to singing onset detection. Third, feature-level and decision-level fusion are employed to fuse different features for a higher level of performance. Evaluated on a recorded singing database, the proposed approach outperforms state-of-the-art onset detection algorithms significantly.|
|Source Title:||ISMIR 2008 - 9th International Conference on Music Information Retrieval|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 13, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.