Song lyrics have become simpler and more repetitive over the last five decades.

Music is ubiquitous in our everyday lives, and lyrics play an integral role when we listen to music. The complex relationships between lyrical content, its temporal evolution over the last decades, and genre-specific variations, however, are yet to be fully understood. In this work, we investigate the dynamics of English lyrics of Western, popular music over five decades and five genres, using a wide set of lyrics descriptors, including lyrical complexity, structure, emotion, and popularity. We find that pop music lyrics have become simpler and easier to comprehend over time: not only does the lexical complexity of lyrics decrease (for instance, captured by vocabulary richness or readability of lyrics), but we also observe that the structural complexity (for instance, the repetitiveness of lyrics) has decreased. In addition, we confirm previous analyses showing that the emotion described by lyrics has become more negative and that lyrics have become more personal over the last five decades. Finally, a comparison of lyrics view counts and listening counts shows that when it comes to the listeners' interest in lyrics, for instance, rock fans mostly enjoy lyrics from older songs; country fans are more interested in new songs' lyrics.

The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts.

Selecting appropriate musical stimuli to induce specific emotions represents a recurring challenge in music and emotion research. Most existing stimuli have been categorized according to taxonomies derived from general emotion models (e.g., basic emotions, affective circumplex), have been rated for perceived emotions, and are rarely defined in terms of interrater agreement. To redress these limitations, we present research that served in the development of a new interactive online database, including an initial set of 364 music excerpts from three different genres (classical, pop, and hip/hop) that were rated for felt emotion using the Geneva Emotion Music Scale (GEMS), a music-specific emotion scale. The sample comprised 517 English- and German-speaking participants and each excerpt was rated by an average of 28.76 participants (SD = 7.99). Data analyses focused on research questions that are of particular relevance for musical database development, notably the number of raters required to obtain stable estimates of emotional effects of music and the adequacy of the GEMS as a tool for describing music-evoked emotions across three prominent music genres. Overall, our findings suggest that 10-20 raters are sufficient to obtain stable estimates of emotional effects of music excerpts in most cases, and that the GEMS shows promise as a valid and comprehensive annotation tool for music databases.

Support the underground: characteristics of beyond-mainstream music listeners.

Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup's openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.