Improving the Performance of Pitch Estimators
We are looking to use pitch estimators to provide an accurate high-resolution pitch track for resynthesis of musical audio. We found that current evaluation measures such as gross error rate (GER) are not suitable for algorithm selection. In this paper we examine the issues relating to evaluating pitch estimators and use these insights to improve performance of existing algorithms such as the well-known YIN pitch estimation algorithm.
Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members, $5 for AES members and is free for E-Library subscribers.