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

Michael Fell 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : 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 thesis, we argue 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, we show for different textual aspects (their structure, content and perception) how Natural Language Processing methods can be adapted and successfully applied to lyrics. For the structural aspect of lyrics, we derive a structural description of it by introducing a model that efficiently segments the lyrics into its characteristic parts (e.g. intro, verse, chorus). In a second stage, we represent the content of lyrics by means of summarizing the lyrics in a way that respects the characteristic lyrics structure. Finally, on the perception of lyrics we investigate the problem of detecting explicit content in a song text. This task proves to be very hard and we show that the difficulty partially arises from the subjective nature of perceiving lyrics in one way or another depending on the context. Furthermore, we touch on another problem of lyrics perception by presenting our preliminary results on Emotion Recognition. As a result, during the course of this thesis we have created the annotated WASABI Song Corpus, a dataset of two million songs with NLP lyrics annotations on various levels.
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Submitted on : Friday, May 15, 2020 - 12:21:52 PM
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  • HAL Id : tel-02587910, version 1


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. English. ⟨tel-02587910v1⟩



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