Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.
The task of annotating song lyrics is also related to metaphor processing. As annotators often explain metaphors used in song lyrics, the Genius dataset can serve as a resource to study computational modeling of metaphors @cite_19 .
@cite_19
2153844722 116271097 131896369 2136129419
We explore the use of song lyrics for automatic indexing of music. Using lyrics mined from the Web, we apply a standard text processing technique to characterize their semantic content. We then determine artist similarity in this space. We found lyrics can be used to discover natural genre clusters. Experiments on a publicly available set of 399 artists showed that determining artist similarity using lyrics is better than random, but inferior to a state-of-the-art acoustic similarity technique. However the approaches made different errors, suggesting they could be profitably combined We address the issue of generating texts in the style of an existing author, that also satisfy structural constraints imposed by the genre of the text. We focus on song lyrics, for which structural constraints are well-defined: rhyme and meter. Although Markov processes are known to be suitable for representing style, they are difficult to control in order to satisfy non-local properties, such as structural constraints, that require long distance modeling. We show that the framework of Constrained Markov Processes allows us to precisely generate texts that are consistent with a corpus, while being controllable in terms of rhymes and meter, a result that no other technique, to our knowledge, could achieve to date. Controlled Markov processes consist in reformulating Markov processes in the context of constraint satisfaction. We describe how to represent stylistic and structural properties in terms of constraints in this framework and we provide an evaluation of our method by comparing it to both pure Markov and pure constraint-based approaches. We show how this approach can be used for the semi-automatic generation of lyrics in the style of a popular author that has the same structure as an existing 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 account. Cultural information influences how we experience music, as does the songs’ text and its sound. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyrics, may thus be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents – many lyrics are for example composed in rhyming verses, and may have different frequencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use ‘slang’ language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word verse length, and the amount of repetative text. In this paper, we present a novel set of features developed for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classification on a test collection in order to demonstrate our analysis. Music is composed to be emotionally expressive, and emotional associations provide an especially natural domain for indexing and recommendation in today's vast digital music libraries. But such libraries require powerful automated tools, and the development of systems for automatic prediction of musical emotion presents a myriad challenges. The perceptual nature of musical emotion necessitates the collection of data from human subjects. The interpretation of emotion varies between listeners thus each clip needs to be annotated by a distribution of subjects. In addition, the sharing of large music content libraries for the development of such systems, even for academic research, presents complicated legal issues which vary by country. This work presents a new publicly available dataset for music emotion recognition research and a baseline system. In addressing the difficulties of emotion annotation we have turned to crowdsourcing, using Amazon Mechanical Turk, and have developed a two-stage procedure for filtering out poor quality workers. The dataset consists entirely of creative commons music from the Free Music Archive, which as the name suggests, can be shared freely without penalty. The final dataset contains 1000 songs, each annotated by a minimum of 10 subjects, which is larger than many currently available music emotion dataset.