Contribute

If you have implemented a new tempo estimation approach or created a new reference dataset, please contribute your data.

References

If you have created a revised set of annotations for an existing dataset or a completely new dataset, please create corresponding JAMS and a pull request.

Additional annotations for existing datasets should be added to the existing .jams files. Please make sure to include both the data and the code that adds your data to the jams.

Example

To add new reference annotations to the dataset ballroom

  1. Create a new folder under annotations/references/ballroom/ named after the publication that features your new annotations, e.g., smith2018. If your annotations were not released in a scientific publication, come up with some other reasonable name.

  2. Place your original annotations (that don’t have to be JAMS) into the new folder.

  3. Add code to tempo_eval/parser/ballroom.py that creates .jams files containing all known annotations for the dataset. These .jams must be placed into the folder annotations/references/ballroom/jams/

Should you want to add a new dataset, please follow the recipe above with the exception that you should create a new Python file named your_dataset_name.py, which reads your original annotations and creates .jams files in the appropriate folder.

Please read about how to annotate your JAMS in Annotation Metadata.

Estimates

An essential characteristic of tempo_eval is that it does not only contain reference datasets, but also estimates. Contributing your estimates allows other researchers to compare to your work without having to re-run all your experiments. At the same time, you can compare your results with a wide variety of existing approaches.

Example

To add new estimates for the dataset ballroom

  1. Create a new folder under annotations/estimates/ballroom/ named after the publication that features your new approach, e.g., smith2018. If your estimates were not released in a scientific publication, come up with some other reasonable name that allows others to recognize your system, e.g., a product name.

  2. Oftentimes one system can be run with different parameters or one publication features multiple approaches. In your smith2018 folder, create subfolders for each of these approaches or parameter sets. The subfolder’s name could simply be a version number version_3_2_1 or just default. It’s highly recommended to include information in the JAMS as well.

  3. Place your original annotations (that don’t have to be JAMS) into the new subfolder.

  4. If your annotations aren’t yet in the JAMS format, convert them. tempo_eval provides a script called convert2jams, which makes it easy to convert popular formats like CSV. Before you do this, please read about how to properly annotate your JAMS in Annotation Metadata.

Annotation Metadata

Please ensure that your JAMS contain suitable annotation_metadata. The bare minimum for tempo_eval is a corpus name and a version that is unique per corpus. The corpus name should match the annotations/references/-folder name, if possible, e.g., ballroom.

Because the annotator object is of type Sandbox, it supports any kind of dict. tempo_eval honors two special keys: bibtex and ref_url. Both keys aim at giving credit to your work. While ref_url lets you specify a simple URL to your own website, bibtex lets you specify a minimal bibtex entry. Should you choose to add such an entry, you might also want to consider adding it to tempo_eval/references.bib.

Example for ref_url and bibtex annotator keys.
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[...]
"annotation_metadata": {
    "curator": {
        "name": "Simon Dixon",
        "email": "s.e.dixon@qmul.ac.uk"
    },
    "annotator": {
        "bibtex": "@article{Gouyon2006,\nAuthor = \"Gouyon, Fabien and Klapuri, Anssi P. and Dixon, Simon and Alonso, Miguel and Tzanetakis, George and Uhle, Christian and Cano, Pedro\",\nJournal = \"IEEE Transactions on Audio, Speech, and Language Processing\",\nNumber = \"5\",\nPages = \"1832--1844\",\nTitle = \"An experimental comparison of audio tempo induction algorithms\",\nVolume = \"14\",\nYear = \"2006\"\n}\n",
        "ref_url": "http://mtg.upf.edu/ismir2004/contest/tempoContest/node5.html"
    },
    "version": "1.0",
    "corpus": "ballroom",
    "annotation_tools": "",
    "annotation_rules": "",
    "validation": "",
    "data_source": "BallroomDancers.com, checked by human"
}
[...]

convert2jams

convert2jams is a simple command line script that converts your CSV, TSV, JSON or plain text tempo annotations into JAMS.

For detailed usage information please run:

$ convert2jams --help

Here’s an example that turns text files into jams.

$ convert2jams -d out_dir -i text_file_dir -a audio_file_dir \
    -c corpus_name -v annotation_version

For this to work, each text file located in text_file_dir must contain only tempo values for a single audio file. These tempo values can either be single values or a MIREX-style triplet. The text file names must correspond to the audio file names. For example, the annotation for the audio file my_track.wav should be in a text file called my_track.txt. All audio files should be under audio_file_dir.

Note that each jam file should at least be annotated with a corpus name and a version. More parameters, like bibtex, curator, etc. can be easily specified via the command line.

Alternatively, if you already have a jam file that contains a suitable annotation_metadata-block, you can use it as template using the --template parameter:

$ convert2jams -d out_dir -i text_file_dir -a audio_file_dir \
    --template template.jams