bayesbeat is a MATLAB package for metrical structure analysis of mucial audio signals. It is based on the dynamic bar pointer model first proposed in [1]. The model was later extended by various authors.

It includes algorithms for inference with Hidden Markov Models (HMMs) and Particle Filter models (PF). For detailed information about the algorithms, please also see the References section.

License

The package has two licenses, one for source code and one for model/data files.

Source code

Unless indicated otherwise, all source code files are published under the BSD license. For details, please see the LICENSE file.

Model and data files

Unless indicated otherwise, all model and data files are distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.

If you want to include any of these files (or a variation or modification thereof) or technology which utilises them in a commercial product, please contact Gerhard Widmer.

Installation:

To use the much faster viterbi MEX-file, you have to build it first:

1. Within the MATLAB gui, go to the folder src/@HiddenMarkovModel.
2. Then, execute mex viterbi_cpp.cpp.

Package structure

The package has a very simple structure, divided into the following folders:

• /apps this folder includes applications (i.e. executable algorithms)
• /doc documentation
• /examples examples of how to use the package
• /scr source code of the package
• /tests tests

Getting started

The bayesbeat package is best explored by looking at the examples folder. E.g., you can try the following examples:

• Example 1: Compute beats using a pretrained HMM model
• Example 2: Compute beats using a pretrained PF model
• Example 3/4: Learn the HMM observation model parameters from training data and set up a HMM
• Example 5: Learn the HMM observation model parameters from training data and set up a PF model

Contact:

For comments and bug reports please contact:
Florian Krebs

References

[1] Whiteley, N., Cemgil A. T., and Godsill S.. Bayesian Modelling of Temporal Structure in Musical Audio. Proceedings of the 14th International Conference on Music Information Retrieval (ISMIR). 2006.

[2] Whiteley, N., Cemgil, A. T., Godsill, S.. Sequential inference of rhythmic structure in musical audio. Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2007.

[3] Krebs, F., Böck S., and Widmer G.. Rhythmic Pattern Modelling for Beat and Downbeat Tracking from Musical Audio. Proceedings of the 14th International Conference on Music Information Retrieval (ISMIR), Curitiba. 2013.

[4] Krebs, F., Holzapfel, A., Cemgil, A. T., and Widmer, G.. Inferring Metrical Structure in Music Using Particle Filters. In IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2015.

[5] Holzapfel, A., Krebs, F., Srinivasamurthy, A.. Tracking the "odd": Meter inference in a culturally diverse music corpus. Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR), 2014.

[6] Srinivasamurthy, A., Holzapfel, A., Cemgil, A., Serra, X.. Particle Filters for Efficient Meter Tracking with Dynamic Bayesian networks. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), 2015.

[7] Krebs, F., Böck, S., and Widmer, G.. An Efficient State Space Model for Joint Tempo and Meter Tracking. In Proceedings of 16th International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain, 2015.

[8] Böck, S., Krebs, F., and Schedl., M.. Evaluating the Online Capabilities of Onset Detection Methods. In Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal, 2012.