An Analysis Method for Metric-Level Switching in Beat Tracking
Journal
IEEE Signal Processing Letters
Journal Volume
29
Date Issued
2022-01-01
Author(s)
Abstract
For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this letter, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when being utilized alongside existing metrics, and discuss the new insights that can be gained.
Subjects
Beat tracking | evaluation metrics; eess.AS; eess.AS; Computer Science - Sound
Description
Accepted to IEEE Signal Processing Letters (Oct. 2022)
Type
journal article