bandcamp-librarian 0.1.0

This project detects stylistic tendencies in the Bandcamp libraries of electronic dance music labels by defining clusters based on Beatport's subgenre categories as of Jan 2021. To deploy the web interface on 0.0.0.0:8080/, enter bandcamplibrarian-on from the command line after installation.

bandcamp_dance_librarian

Bandcamp Dance Librarian—detecting stylistic tendencies in the Bandcamp libraries of electronic dance music labels

This project classifies Bandcamp’s electronic dance music label libraries by defining clusters based on Beatport’s subgenre taxonomy as of Jan 2021. It also offers a comparison between Beatport’s taxonomy and Bandcamp’s folksonomies (user defined tags).

A working demo of the project is available under http://18.198.194.11:8080 (use http://18.198.194.11:8080/admin for the admin interface)

You can install the project from the command line by entering: pip install bandcamp-librarian

After installation, enter bandcamplibrarian-on from the command line to switch on the docker service and access the web interface running on 0:0:0:0/8080. To switch off the service, enter: bandcamplibrarian-off

• Free software: MIT license

Methodology

The subgenre classification algorithm follows the methodology outlined in Caparrini et al. (2020), the datasets of which encompass audio features extracted from Beatport’s Top-100 lists covering a range of electronic dance music subgenres.

This project relies on Beatport’s Jan 2021 Top-100 lists, covering 33 categories. These included DJ Tools, which was not an actual subgenre but a collection of sound samples destined for DJs and producers - therefore, it was not included in this project.

The Electronica category, a loose collection of tracks reated to various subgenres, was replaced with 100 tracks (selected from the full range of release dates while excluding duplicate artists) out of the 439 tracks labelled as Ambient in the Beatport catalogue. Ambient is more defined in terms of intrinsic musical qualities than Electronica, while being listed as a subgenre of Electronica on Beatport. Importantly, many electronic dance music releases feature ambient tracks or influences, which warranted its inclusion into the dataset.

92 audio features were extracted using pyAudioAnalysis and Essentia from the track samples provided with the lists. The resulting 3200-tracks dataset contained 17 duplicates. These were replaced with tracks/features extracted from Beatport’s Dec 2020 Top-100 lists (the top track(s) from the corresponding subgenres were selected).

Location of the final dataset in the project folder structure: dataset-model-predictor/beatport_2021jan.csv

Based on this dataset, an sklearn ExtraTreesRegressor model was trained that classifies tracks based on their 92 audio features. The model was tuned up to a 10-fold cross validation score of 0.481 (with a standard deviation of 0.069) and an F1 score of 0.545. See: dataset-model-predictor/model_build.ipynb

Considering the number of subgenres, these results are in the range of the performance scores provided by Caparrini et al. (2020): their k=10 validation accuracies are 0.590 +/- 0.026 for the classifier trained on Beatport Set 1 (2016) containing 23 subgenres; and 0.482 +/- 0.024 for the classifier trained on Beatport Set 2 (2018) contained 29 subgenres. According to Caparrini et al. (2020) these are fair results when taking into account the standard features extracted, the high number of subgenres and subgenre proximities.

The clustering algorithm uses weights calculated from the feature importances of the classifier. For each audio feature, the weight is first calculated from the square root of the feature importance; finally all weights are divided by the sum of weights to achieve a sum of 1. The weights are applied after normalizing the data with MinMaxScaler (cf. Kaufman and Rousseeuw 1990:13-14).

The K-means clustering algorithm is then run multiple (10) times on the transformed data. During each iteration, the cluster centroids are fed to the classifier; the three highest probability values (pertaining to three subgenres) provided by the classifier are then added together with a weight of 0.5 applied to the highest value. Finally, the model with the highest cumulative sum of probabilities across all clusters is stored. Although this optimisation process is somewhat arbitrary, it is meant to ensure that the cluster centroids are crystallised around the classifier’s subgenre categories; the weight is applied to decrease the importance of the highest value in selecting the final model, thus resulting in a better amalgam of subgenres. If the user selects automatic cluster number recommendation, this whole process is repeated for cluster numbers ranging from 1 to 6, and finally the cluster number located at the elbow of the inertia curve is selected. If no elbow can be defined in 5 consecutive attempts, the number of clusters is set to 4.

A PDF report is generated with the three highest subgenre probability values pertaining to the centroids and three track examples (i.e. tracks closest to their centroids based on Euclidean distance metering) in each cluster, with links to their Bandcamp pages and their associated Bandcamp folksonomies.

References

Antonio Caparrini, Javier Arroyo, Laura Pérez-Molina and Jaime Sánchez-Hernández. 2020. “Automatic subgenre classification in an electronic dance music taxonomy.” Journal of New Music Research 49(12):1-16.

Leonard Kaufman and Peter J. Rousseeuw. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, New Jersey: John Wiley & Sons.

Features/Pipeline

The project pipeline is running in Docker containers featuring a Bandcamp scraper, analyser, Postgres database and a Flask-powered user/admin website.

The classifier/clusterer interface is based on a Flask application running on 0:0:0:0/8080.

New Bandcamp labels/libraries can be added by using the admin interface on 0:0:0:0/8080/admin.

The track audio features and other attributes (incl. low-res release cover data) are stored in a Postgres database.

The labels Postgres table contains a shortlist of labels including the full label name and the number of files already processed.

It is also possible to manually import the tracks and labels database by running the csvimport.py python script located in the /config folder; the csvexport.py script in the same folder can be used for exporting the database.

The config.csv file in the /config folder defines the pipeline scraping/analysis settings as well as the size limit for the scraped files (by default 20MB, approx. 20 mins long MP3 track).

To start scraping manually (i.e., not through the web interface), you can edit the config.csv: set “scraping” to 0 and “bclabel” to the bandcamp label name found in the Bandcamp url (for example, “polegroup” for “https://polegroup.bandcamp.com/”). To stop scraping manually, set “scraping” to 0 and “bclabel” to “_none” in config.csv.

The classification model can be dynamically modified by replacing the beatport_classifier.sav file in the /config folder.

Credits

The Bandcamp scraper is based on SoundScrape / Rich Jones Miserlou / SoundScrape - https://github.com/Miserlou/SoundScrape

This package was created with Cookiecutter and the Spiced Academy Cookiecutter PyPackage project template.

History

0.1.0 (2021-02-11)

• First release on PyPI.