Reliability Assessment Of Singing Voice F0-Estimates Using Multiple Algorithms

Over the last decades, various conceptually different approaches for fundamental frequency (F0) estimation in monophonic audio recordings have been developed. The algorithms’ performances vary depending on the acoustical and musical properties of the input audio signal. A common strategy to assess the reliability (correctness) of an estimated F0-trajectory is to evaluate against an annotated reference. However, such annotations may not be available for a particular audio collection and are typically labor-intensive to generate. In this work, we consider an approach to automatically assess the reliability of F0-trajectories estimated from monophonic singing voice recordings. As main contribution, we propose three reliability indicators that are based on the outputs of multiple algorithms. Besides providing a mathematical description of the indicators, we analyze the indicators’ behavior using a set of annotated vocal F0-trajectories. Furthermore, we show the potential of the proposed indicators for exploring unlabeled audio collections.