An effective, simple tempo estimation method based on self-similarity and regularity

Tempo estimation is a fundamental problem in music information retrieval. It also forms the basis of other types of rhythmic analysis such as beat tracking and pattern detection. There is a large body of work in tempo estimation using a variety of different approaches that differ in their accuracy as well as their complexity. Fundamentally they take advantage of two properties of musical rhythm: 1) the music signal tends to be self-similar at periodicities related to the underlying rhythmic structure, 2) rhythmic events tend to be spaced regularly in time. We propose an algorithm for tempo estimation that is based on these two properties. We have tried to reduce the number of steps, parameters and modeling assumptions while retaining good performance and causality. The proposed approach outperforms a large number of existing tempo estimation methods and has similar performance to the best-performing ones. We believe that we have conducted the most comprehensive evaluation to date of tempo induction algorithms in terms of number of datasets and tracks.