A pairwise approach to simultaneous onset/offset detection for singing voice using correntropy
This addresses the problem of accurate note segmentation in singing voice analysis for applications like music information retrieval, though it appears incremental as it builds on existing onset detection methods.
The paper tackles the problem of detecting precise note onset and offset locations in singing voice signals by proposing a pairwise detection method using correntropy and a novel peak-picking algorithm, achieving performance significantly better than or comparable to state-of-the-art techniques.
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.