Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/42160
Title: Multiple-feature fusion based onset detection for solo singing voice
Authors: Toh, C.C.
Zhang, B.
Wang, Y. 
Issue Date: 2008
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/42160
ISBN: 9780615248493
Appears in Collections:Staff Publications

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