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If you find a broken or outdated link, please let me know.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.
A more comprehensive list of all kinds of MIR-related datasets can be found at www.audiocontentanalysis.org.
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:
The accuracy results for [2] reported in the ISMIR 2018 publication [3], have been erroneously computed with a buggy version of the public estimator (v0.0.1). Please refer to [2] for correct results. Also, please use the latest version (v0.0.4 or later) when comparing your method with [2].
[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. [slides] [code]
[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. [slides]
[6] Hendrik Schreiber, Meinard Müller, Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters. In Proceedings of the Sound and Music Computing Conference (SMC), Málaga, Spain, May 2019. [code]
[7] Hendrik Schreiber, Julián Urbano, and Meinard Müller, Music Tempo Estimation: Are We Done Yet? Transactions of the International Society for Music Information Retrieval (TISMIR), 3(1): 111–125, 2020. [code|report]
[8] Hendrik Schreiber, Frank Zalkow, Meinard Müller, Modeling and Estimating Local Tempo: A Case Study on Chopin’s Mazurkas. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR), Montréal, QC, Canada, Oct. 2020. [poster|slides]
