Language Classification of Music Using Metadata

Abstract: The purpose of this study was to investigate how metadata from Spotify could be used to identify the language of songs in a dataset containing nine languages. Features based on song name, album name, genre, regional popularity and vectors describing songs, playlists and users were analysed individually and in combination with each other in different classifiers. In addition to this, this report explored how different levels of prediction confidence affects performance and how it compared to a classifier based on audio input. A random forest classifier proved to have the best performance with an accuracy of 95.4% for the whole data set. Performance was also investigated when the confidence of the model was taken into account, and when only keeping more confident predictions from the model, accuracy was higher. When keeping the 70% most confident predictions an accuracy of 99.4% was achieved. The model also proved to be robust to input of other languages than it was trained on, and managed to filter out unwanted records not matching the languages of the model. A comparison was made to a classifier based on audio input, where the model using metadata performed better on the training and test set used. Finally, a number of possible improvements and future work were suggested.