NLP for Music Information Retrieval

Michael Kurt Fell presents an interesting analysis of Lyrics Structure and Content.

Title: Natural Language Processing for Music Information Retrieval: Deep Analysis of Lyrics Structure and Content

Applications in Music Information Retrieval and Computational Musicology have traditionally relied on features extracted from the music content in the form of audio, but mostly ignored the song lyrics. More recently, improvements in fields such as music recommendation have been made by taking into account external metadata related to the song. In this talk, he will demonstrate that extracting knowledge from the song lyrics is the next step to improve the user’s experience when interacting with music. To extract knowledge from vast amounts of song lyrics, he will show for different textual aspects (their structure, content, and perception) how Natural Language Processing (NLP) methods can be adapted and successfully applied to lyrics. For the structural aspect of lyrics, a structural description of it is obtained by introducing a model that efficiently segments the lyrics into its characteristic parts (e.g. intro, verse, chorus). In a second stage, the content of lyrics is represented by means of summarizing the lyrics in a way that respects the characteristic lyrics structure. Finally, on the perception of lyrics he faced the problem of detecting explicit content in a song text. This task proves to be very hard and he will show that the difficulty partially arises from the subjective nature of perceiving lyrics in one way or another depending on the context. As a consequence of this work, he has also created the annotated WASABI Song Corpus, a dataset of two million songs with NLP lyrics annotations on various levels.

Related Work: Michael Fell. Natural Language Processing for Music Information Retrieval: Deep Analysis of Lyrics Structure and Content. Computation and Language [cs.CL]. Université Côte D’Azur, 2020.

When: On 26th February at 11.30 am


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