We present a novel approach for analysing and classifying lyrics, experimenting both with
ngram models and more sophisticated features that model different dimensions of a song …

How individuals perceive music is influenced by many different factors. The audible part of a
piece of music, its sound, does for sure contribute, but is only one aspect to be taken into …

Algorithms that can understand and interpret characteristics of music, and organise them for
and recommend them to their users can be of great assistance in handling the ever growing …

In many areas multimedia technology has made its way into mainstream. In the case of
digital audio this is manifested in numerous online music stores having turned into profitable …

This paper describes experimental research investigating the genre classification utility of
combining features extracted from lyrical, audio, symbolic and cultural sources of musical …

Taylor Swift is widely recognized for her song writing skills, known for crafting relatable
lyrics, catchy tunes, and personal storytelling that draws from her experiences and emotions …

We present LyricAlly, a prototype that automatically aligns acoustic musical signals with their
corresponding textual lyrics, in a manner similar to manually-aligned karaoke. We tackle this …

We present an algorithm that allows for indexing music by topic. The application scenario is
an information retrieval system into which any song with known lyrics can be inserted and …

Each artist has their own taste for topics of lyrics such as “love” and “friendship.” Considering
such artist's taste brings new applications in music information retrieval: choosing an artist …

Automatic lyrics-to-audio alignment techniques have been drawing attention in the last years
and various studies have been made in this field. The objective of lyrics-to-audio alignment …