Connecting the Last.fm Dataset to LyricWiki and MusicBrainz. Lyrics-based experiments in genre classification

Abstract

Music information retrieval has lately become an important field of information retrieval, because by profound analysis of music pieces important information can be collected: genre labels, mood prediction, artist identification, just to name a few. The lack of large-scale music datasets containing audio features and metadata has lead to the construction and publication of the Million Song Dataset (MSD) and its satellite datasets. Nonetheless, mainly because of licensing limitations, no freely available lyrics datasets have been published for research. In this paper we describe the construction of an English lyrics dataset based on the Last.fm Dataset, connected to LyricWiki’s database and MusicBrainz’s encyclopedia. To avoid copyright issues, only the URLs to the lyrics are stored in the database. In order to demonstrate the eligibility of the compiled dataset, in the second part of the paper we present genre classification experiments with lyrics-based features, including bagof-n-grams, as well as higher-level features such as rhyme-based and statistical text features. We obtained results similar to the experimental outcomes presented in other works, showing that more sophisticated textual features can improve genre classification performance, and indicating the superiority of the binary weighting scheme compared to tf–idf.

Predicting spotify audio features from Last.fm tags

“…In a similar direction, Bodó and Szilágyi generated a dataset for lyrics genre classification by combining Last.fm tags with MusicBrainz data [4]. MusicBrainz is an online database of music editorial metadata 1 .…”

“…Another publicly available dataset is the Spotify Audio Features Kaggle dataset 4 . This dataset contains more than 116,000 unique tracks, and includes audio features for each track.…”

AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

“…It collected the data of seven well-known authoritative foreign music communities, sorted out and analyzed the data, and provided researchers with offline datasets and analysis results obtained by various algorithms. e offline dataset given by Last.fm [32] is mostly used for the optimization method data in this subsection. e offline dataset given by Last.fm is separated into a training set and a test set, with the training set accounting for 80% of the dataset and the test set accounting for 20%.…”