Beat Critic: Beat Tracking Octave Error Identification By Metrical Profile Analysis

Computational models of beat tracking of musical audio have been well explored, however, such systems often make “octave errors”, identifying the beat period at double or half the beat rate than that actually recorded in the music. A method is described to detect if octave errors have occurred in beat tracking. Following an initial beat tracking estimation, a feature vector of metrical profile separated by spectral subbands is computed. A measure of subbeat quaver (1/8th note) alternation is used to compare half time and double time measures against the initial beat track estimation and indicate a likely octave error. This error estimate can then be used to re-estimate the beat rate. The performance of the approach is evaluated against the RWC database, showing successful identification of octave errors for an existing beat tracker. Using the octave error detector together with the existing beat tracking model improved beat tracking by reducing octave errors to 43% of the previous error rate. 1. STRUCTURAL LEVELS IN BEAT PERCEPTION The psychological and computational representation of listeners experience of musical time is of great application to music information retrieval. Correctly identifying the beat rate (tactus) facilitates further understanding of the importance of other elements in musical signals, such as the relative importance of tonal features. Considerable research has proposed theories of an hierarchical structuring of musical time [12–14, 18, 20, 27], with the favouring of particular temporal levels. The tactus has been shown to be influenced by temporal preference levels [10], proposed as a resonance or inertia to variation [25]. At the metrical level 1 , [21] argue that preestablished mental frameworks (“schemas”) for musical meter are used during listening. They found a significant difference in performance between musicians and non-music1 A periodic repetition of perceived accentuation, notated in music as