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Instructions for reproducing the research described in the paper "Tempo Estimation for Music Loops and a Simple Confidence Measure"

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# Tempo Estimation for Music Loops and a Simple Confidence Measure

This repository contains code and instructions for reproducing the research described in the 
paper *Font, F., & Serra, X. (2016). Tempo Estimation for Music Loops and a Simple Confidence 
Measure. In Int. Conf. on Music Information Retrieval (ISMIR)*. The full text of the 
paper can be [found here](http://mtg.upf.edu/node/3479).

In order to run the experiments described in the paper you'll need to set up the 
datasets and analyze its content. You should create a Python virtual environment and install the 
requirements listed in ```requirements.txt```. In addition, you'll need to 
install [ffmpeg](https://www.ffmpeg.org) (for audio conversion) and, optionally, 
[rabbitMQ](rabbitMQ) (needed for paralelizing analysis using [Celery](https://docs.getsentry.com/hosted/) 
distributed task manager). Then you should follow instructions below:

* [Instructions for setting up datasets](docs/create_dataset.md)
 * [Instructions for setting up analysis algorithms](docs/setting_up_algorithms.md)
 * [Instructions for analyzing the datasets](docs/analyze_dataset.md)

Once datasets are set up and audio analysis has been carried out, you can open the following 
IPython notebooks which contain the code to generate the results and plots shown in the paper:

* [Datasets](Datasets.ipynb): information and statistics about the datasets, corresponds to Section 4.1 of the paper.
 * [Confidence measure](Confidence%20measure.ipynb): description of the confidence measure with examples and code, corresponds to Section 3 of the paper.
 * [Tempo estimation results](Tempo%20estimation%20results.ipynb): evaluation of the different tempo estimation algorithms and confidence measure, corresponds to Section 5 of the paper.
 
**UPDATE**: we implemented Percival's BPM estimation method in Essentia (see [PercivalBpmEstimator  algorithm](https://github.com/MTG/essentia/blob/master/src/algorithms/rhythm/percivalbpmestimator.cpp)). The following notebooks compare the results of the Essentia implementation and the original Python implementation provided by the authors ([notebook1](https://github.com/ffont/ismir2016/blob/master/Compare%20Percival14%20variations.ipynb), [notebook2](https://github.com/ffont/ismir2016/blob/master/Percival14Mod%20vs%20Percival14_essentia.ipynb)).

ismir2016

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