Tempo Estimation

This page serves as a further material site for the publications mentioned below. As such it contains links to binaries, datasets, benchmarks, etc.

If you find a broken or outdated link, please let me know. Thanks.

Downloads/Software

Datasets

A more comprehensive list of all kinds of MIR-related datasets can be found at www.audiocontentanalysis.org.

Comparisons & Benchmarks

Tools & Applications

Errata

The Accuracy1 results reported in the ISMIR 2017 publication [2] have been erroneously computed with a 8% instead of 4% tolerance. This does in no way change the main message of the paper, that Accuracy1 can be improved through the proposed post-processing procedure.
Corrected results are shown below:

References

[1] Hendrik Schreiber, Meinard Müller. Exploiting Global Features for Tempo Octave Correction. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 2014.

[2] Hendrik Schreiber, Meinard Müller. A post-processing procedure for improving music tempo estimates using supervised learning. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China, Oct. 2017.

[3] Hendrik Schreiber, Meinard Müller. A single-step approach to musical tempo estimation using a convolutional neural network. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, Sept. 2018.

[4] Hendrik Schreiber. Technical Report: Tempo and Meter Estimation for Greek Folk Music Using Convolutional Neural Networks and Transfer Learning. 8th International Workshop on Folk Music Analysis (FMA), Thessaloniki, Greece, June 2018.

[5] Hendrik Schreiber, Meinard Müller. A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, Sept. 2018.

Other research.