  In this paper, we propose a novelmethod to search for precise locations of
paired note onset and offset in a singing voice signal. In comparison with the
existing onset detection algorithms,our approach differs in two key respects.
First, we employ Correntropy, a generalized correlation function inspired from
Reyni's entropy, as a detection function to capture the instantaneous flux
while preserving insensitiveness to outliers. Next, a novel peak picking
algorithm is specially designed for this detection function. By calculating the
fitness of a pre-defined inverse hyperbolic kernel to a detection function, it
is possible to find an onset and its corresponding offset simultaneously.
Experimental results show that the proposed method achieves performance
significantly better than or comparable to other state-of-the-art techniques
for onset detection in singing voice.
