Charting the Universe of Metal Music Lyrics and Analyzing Their Relation to Perceived Audio Hardness

Abstract

We analyze the relationship between the musical and the lyrical content of metal music by combining automated audio feature extraction and quantitative text analysis on a corpus of 124,288 song lyrics from this genre. Based on this text corpus, a topic model is first constructed using Latent Dirichlet Allocation (LDA). For a subsample of 503 songs, scores for predicting perceived musical hardness (heaviness) are extracted using an audio feature model and corroborated via listening tests. By combining both audio feature and text analysis, we (1) offer a comprehensive overview of the lyrical topics present within the metal genre and (2) are able to establish whether levels of hardness are associated with the occurrence of particularly harsh (and other) textual topics. Twenty typical topics are identified and projected into a topic space using multidimensional scaling (MDS). Positive correlations are found between musical hardness and textual topics dealing with “brutal death,” “dystopia,” “archaisms and occultism,” “religion and satanism,” “battle,” and “(psychological) madness,” while there is a negative association with topics like “personal life” and “love and romance.” Further, we show different prevalences of the identified topics within different metal subgenres. In summary, this article presents the first comprehensive computational corpus study of musical and lyrical content in metal music.

Keywords

• metal music
• topic modeling
• latent dirichlet allocation
• audio feature extraction
• hardness