An Analysis Method for Metric-Level Switching in Beat Tracking

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 paper, 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.

Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

“…Such a "metric-level switching" behavior, however, cannot be reflected by the current evaluation metrics. We have recently proposed an analysis method [53] for gaining a better understanding of such issues. More results and discussions can be found in our GitHub repository.…”

Dynamic Time Signature Recognition, Tempo Inference, and Beat Tracking Through the Metrogram Transform

“…Given the inherent subjectivity in time signature recognition [1], the proposed posterior exhibits multiple local maxima dependent on hyperparameter selections. This arises primarily due to polyrhythmic and polymetric elements, prevalent in genres like jazz [3]. For instance, a waltz in 3/4 could be perceived as 6/8, 12/8, or 2/2 due to a multi-layered rhythmic hierarchy (as observed in Figure 4).…”

“…Automated music transcription is a field of great significance in the music and educational industry, especially for primarily improvised genres, such as jazz. In particular, one of the most significant and challenging tasks is that of time signature inference and beat tracking, especially in metrically ambiguous extracts, a common occurrence across genres [1]- [3]. Inaccurate beat tracking for performances with rubato (time-varying tempo) or multiple metric interpretations, regardless of the accuracy of note detections, will result in poor quality transcriptions given the misalignment of key rhythmic structures in the transcription [4], [5].…”