Michael Fell


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Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the WASABI Song Corpus with Lyrics Annotations
Michael Fell | Elena Cabrio | Elmahdi Korfed | Michel Buffa | Fabien Gandon
Proceedings of the 12th Language Resources and Evaluation Conference

We present the WASABI Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, as their structure segmentation, their topic, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The creation of the resource is still ongoing: so far, the corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. Such corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and segmentation recommendation of songs.


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Song Lyrics Summarization Inspired by Audio Thumbnailing
Michael Fell | Elena Cabrio | Fabien Gandon | Alain Giboin
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Given the peculiar structure of songs, applying generic text summarization methods to lyrics can lead to the generation of highly redundant and incoherent text. In this paper, we propose to enhance state-of-the-art text summarization approaches with a method inspired by audio thumbnailing. Instead of searching for the thumbnail clues in the audio of the song, we identify equivalent clues in the lyrics. We then show how these summaries that take into account the audio nature of the lyrics outperform the generic methods according to both an automatic evaluation and human judgments.

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Comparing Automated Methods to Detect Explicit Content in Song Lyrics
Michael Fell | Elena Cabrio | Michele Corazza | Fabien Gandon
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

The Parental Advisory Label (PAL) is a warning label that is placed on audio recordings in recognition of profanity or inappropriate references, with the intention of alerting parents of material potentially unsuitable for children. Since 2015, digital providers – such as iTunes, Spotify, Amazon Music and Deezer – also follow PAL guidelines and tag such tracks as “explicit”. Nowadays, such labelling is carried out mainly manually on voluntary basis, with the drawbacks of being time consuming and therefore costly, error prone and partly a subjective task. In this paper, we compare automated methods ranging from dictionary-based lookup to state-of-the-art deep neural networks to automatically detect explicit contents in English lyrics. We show that more complex models perform only slightly better on this task, and relying on a qualitative analysis of the data, we discuss the inherent hardness and subjectivity of the task.


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Lyrics Segmentation: Textual Macrostructure Detection using Convolutions
Michael Fell | Yaroslav Nechaev | Elena Cabrio | Fabien Gandon
Proceedings of the 27th International Conference on Computational Linguistics

Lyrics contain repeated patterns that are correlated with the repetitions found in the music they accompany. Repetitions in song texts have been shown to enable lyrics segmentation – a fundamental prerequisite of automatically detecting the building blocks (e.g. chorus, verse) of a song text. In this article we improve on the state-of-the-art in lyrics segmentation by applying a convolutional neural network to the task, and experiment with novel features as a step towards deeper macrostructure detection of lyrics.


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Lyrics-based Analysis and Classification of Music
Michael Fell | Caroline Sporleder
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers