Autocorrelation in meter induction: the role of accent structure

The performance of autocorrelation-based meter induction was tested with two large collections of folk melodies, consisting of approximately 13 000 melodies for which the correct meters were available. The performance was measured by the proportion of melodies whose meter was correctly classified by a discriminant function. Furthermore, it was examined whether including different melodic accent types would improve the classification performance. By determining the components of the autocorrelation functions that were significant in the classification it was found that periodicity in note onset locations was the most important cue for the determination of meter. Of the melodic accents included, Thomassen's melodic accent was found to provide the most reliable cues for the determination of meter. The discriminant function analyses suggested that periodicities longer than one measure may provide cues for meter determination that are more reliable than shorter periodicities. Overall, the method predicted notated meter with an accuracy reaching 96% for binary classification and 75% for classification into nine categories of meter.