  Unaccompanied vocalists usually change the tuning unintentionally and end up
with a higher or lower pitch than the starting point during a long performance.
This phenomenon is called pitch drift, which is dependent on various elements,
such as the skill of the performer, and the length and difficulty of the
performance. In this paper, we propose a computational method for measuring
pitch drift in the course of an unaccompanied vocal performance, using pitch
histogram and DBSCAN clustering.

Sound;Machine Learning;Multimedia;Audio and Speech Processing