Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the WASABI Song Corpus with Lyrics Annotations

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, such as their structure segmentation, their topics, 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.Comment: 10 page

Embarking on a Web Information Extraction project

Web Information Extraction (WIE) is a very popular topic, however we have yet to find a fully operational implementation of WIE, especially in the training courses domain. This paper explores the variety of technologies that can be used for this kind of project and introduces some of the issues that we have experienced. Our aim is to show a different view of WIE, as a reference model for future projects

Confrontation in ‘Confrontation’: A Multimodal Analysis of Bob Marley’s Lyrics

This study investigates multimodally the theme of confrontation in Bob Marley’s posthumous Confrontation album. Specifically, the study analyses how the theme of confrontation is conveyed by the artwork of the album cover design. Besides, the study shows linguistically, literarily and by any other aesthetic ways how the theme of confrontation runs through the album. Findings reveal that the artwork of the album cover design speaks volumes without words and is embedded with conscious sophistication in projecting the theme of confrontation. Linguistically, Marley employs deliberate and conscious fiery lexical items inherently confrontational in projecting the theme of confrontation. Literarily, repetition, allusion, pleonasm and metaphors among others play the key role of confrontation in the Confrontation album. Finally, the name of the album, the name of each song and their numerical placement reveal the other aesthetic ways that the theme of confrontation is projected in Marley’s Confrontation, making the album not just a lyrical genius but a discourse masterpiece

Segmenter les paroles de chansons : détection par réseau de neurones convolutif d’une macrostructure textuelle

International audienceLyrics 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.Les paroles de chansons contiennent des passages qui se répètent et sont corrélés aux répétitionstrouvé dans la musique qui les accompagne. Ces répétitions dans les textes de chansons ontmontré leur utilité pour la segmentation des paroles qui est une étape préalable fondamentale dansla détection automatique des blocs de construction d’une chanson (ex. le refrain, les couplets).Dans cet article, nous améliorons l’état de l’art de la segmentation des paroles en concevant unréseau de neurones convolutif pour cette tâche et expérimentons de nouvelles caractéristiquespour aller vers une détection plus profonde de la macrostructure des paroles

K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling

Lyric translation, a field studied for over a century, is now attracting computational linguistics researchers. We identified two limitations in previous studies. Firstly, lyric translation studies have predominantly focused on Western genres and languages, with no previous study centering on K-pop despite its popularity. Second, the field of lyric translation suffers from a lack of publicly available datasets; to the best of our knowledge, no such dataset exists. To broaden the scope of genres and languages in lyric translation studies, we introduce a novel singable lyric translation dataset, approximately 89\% of which consists of K-pop song lyrics. This dataset aligns Korean and English lyrics line-by-line and section-by-section. We leveraged this dataset to unveil unique characteristics of K-pop lyric translation, distinguishing it from other extensively studied genres, and to construct a neural lyric translation model, thereby underscoring the importance of a dedicated dataset for singable lyric translations

Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the WASABI Song Corpus with Lyrics Annotations

Due to COVID 19 pandemic, the 12th edition is cancelled. Next edition, the 13th, LREC 2022 will take place in Pharo on June 16-24, 2022.International audienceWe 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, such as their structure segmentation, their topics, 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 recommendation of songs. We provide the files of the current version of the WASABI Song Corpus, the models we have built on it as well as updates here: https://github.com/micbuffa/WasabiDataset

A 2 Million Commercial Song Interactive Navigator

International audienceIn this paper, we present a web-based interactive tool for exploring a collection of two million commercially released songs. It gathers song information from a large number of heterogeneous sources, web-based and audio-based, and integrates work from multiple research groups. The resulting tool can be used to request information about a specific song such as lyrics, metadata and chords; to navigate further on to linked external resources such as Discogs, AllMusic, Mu-sicBrainz or a number of streaming providers; or to browse the collection by artist's discographies or band membership. Several Web Audio applications are integrated and use the dataset to enrich the experience

歌詞の談話構造のモデル化

Tohoku University乾健太郎課

Procedural Generation of Musical Metrics Based on Lyrics Analysis

Mais do que a componente semântica e discursiva, as letras musicais contêm geralmente outro tipo de informação, que mais do que com o ato da escrita, tem que ver com o ato da pronúncia. Assumindo que uma letra musical é escrita para posteriormente ser reproduzida verbalmente, há um cuidado para que esse processo nos transmita algo também, completamente diferente daquilo que nos é transmitido pela letra no papel. A sincronia das acentuações fonéticas e lexicais da letra com as componentes musicais em que se insere é disso o maior exemplo. Neste projeto, a proposta é criar um sistema capaz de devolver informação musical para uma dada letra. Mais concretamente, informação relativa à métrica. Para o efeito, utilizarei o CMUdict, um dicionário de informação fonética para a língua inglesa que contém, para cada palavra, a divisão por fonemas com os respectivos marcadores referentes à sua acentuação. Todo o funcionamento do sistema será baseado na linguagem de programação Python, tendo sido todo o código desenvolvido por mim especialmente para o projeto. Para cada letra introduzida, será executada uma análise por versos e cada verso será transformado num template métrico. Todos os versos da letra serão ajustados a cada um dos templates e serão classificados, de forma a perceber-se qual o template que melhor se ajusta à letra em geral. O template com maior pontuação será escolhido como estrutura métrica final.More than the semantic and discursive components, the musical lyrics often contain other information, that more than with the act of writing, has to do with the act of pronunciation. Assuming that the musical lyrics are written to later be reproduced verbally, there is a caution for this process to pass us something too, completely different from what is conveyed by the lyrics on paper. The synchrony of phonetic and lexical accents of the lyrics with the musical components in which it belongs is a great example of that. In this project, the proposal is to create a system able to return music information for a given lyrics. More specifically, information on the metrics. To this end, I will use the CMUdict, a phonetic information dictionary for English language that contains, for each word, the division of its phonemes with the respective markers related to their stress. The entire operation of the system will be based on Python programming language, having all the code been developed by me especially for the project. For each letter entered, it will run an analysis by verses and each verse will become a metric template. All the verses from the lyrics will be adjusted to each of the templates and will be classified in order to select what is the template that best fits the letter in general. The template with the highest score will be chosen as the metric final structure