When we appreciate a piece of music, it is most naturally because of its content, including
rhythmic, tonal, and timbral elements as well as its lyrics and semantics. This suggests that …

With the development of multimodal systems and natural language generation techniques,
the resurgence of multimodal datasets has attracted significant research interests, which …

In this article, we present an extensive survey of music information retrieval (MIR) research
into music playlists. Our survey spans more than 20 years, and includes around 300 papers …

Every type of musical data (audio, symbolic, lyrics, etc.) has its limitations, and cannot
always capture all relevant properties of a particular musical category. In contrast to more …

In this paper, we study the role of serendipity in music playlists. Serendipity is an important
construct in recommendations, and finding an indicator of serendipity in a user-created …

Accurate song success prediction is vital for the music industry, guiding promotion and label
decisions. Early, accurate predictions are thus crucial for informed business actions. We …

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 …

We present a multi-modal genre recognition framework that considers the modalities audio,
text, and image by features extracted from audio signals, album cover images, and lyrics of …

In music information retrieval, genre recognition is the task of automatically assigning genre
labels to a given piece of music. Approaches for this typically employ machine learning …

Music and emotions are inherently intertwined. Humans leave hints of their personality
everywhere, and particularly their music listening behavior shows conscious and …