Streamlined Tempo Estimation Based on Autocorrelation and Cross-correlation With Pulses

Abstract

Algorithms for musical tempo estimation have become increasingly complicated in recent years. These algorithms typically utilize two fundamental properties of musical rhythm: some features of the audio signal are self-similar at periods related to the underlying rhythmic structure, and rhythmic events tend to be spaced regularly in time. We present a streamlined tempo estimation method (stem) that distills ideas from previous work by reducing the number of steps, parameters, and modeling assumptions while retaining good accuracy. This method is designed for music with a constant or near-constant tempo. The proposed method either outperforms or has similar performance to many existing state-of-the-art algorithms. Self-similarity is captured through autocorrelation of the onset strength signal (OSS), and time regularity is captured through cross-correlation of the OSS with regularly spaced pulses. Our findings are supported by the most comprehensive evaluation of tempo estimation algorithms to date in terms of the number of datasets and tracks considered. During the process we have also corrected ground truth annotations for the datasets considered. All the data, the annotations, the evaluation code, and three different implementations (C++, Python, MATLAB) of the proposed algorithm are provided in order to support reproducibility.