Article

Natural language processing of lyrics

Authors:

Fabien GouyonAuthors Info & Claims

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

Pages 475 - 478

https://doi.org/10.1145/1101149.1101255

Published: 06 November 2005 Publication History

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Abstract

We report experiments on the use of standard natural language processing (NLP) tools for the analysis of music lyrics. A significant amount of music audio has lyrics. Lyrics encode an important part of the semantics of a song, therefore their analysis complements that of acoustic and cultural metadata and is fundamental for the development of complete music information retrieval systems. Moreover, a textual analysis of a song can generate ground truth data that can be used to validate results from purely acoustic methods. Preliminary results on language identification, structure extraction, categorization and similarity searches suggests that a lot of profit can be gained from the analysis of lyrics.

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Jasmitha TMeeradevi J S(2024)Artificial Intelligence for Lyric Generator based on Bi-Directional LSTM and Transformers2024 5th International Conference on Circuits, Control, Communication and Computing (I4C)10.1109/I4C62240.2024.10748468(228-234)Online publication date: 4-Oct-2024
https://doi.org/10.1109/I4C62240.2024.10748468
Casanovas-Buliart LAlvarez-Cueva PCastillo C(2024)Evolution over 62 years: an analysis of sexism in the lyrics of the most-listened-to songs in SpainCogent Arts & Humanities10.1080/23311983.2024.243672311:1Online publication date: 11-Dec-2024
https://doi.org/10.1080/23311983.2024.2436723
Khamis MGomaa W(2024)Analysis of the Arab singer Shadia’s lyricsInternational Journal of Data Science and Analytics10.1007/s41060-024-00669-9Online publication date: 9-Nov-2024
https://doi.org/10.1007/s41060-024-00669-9
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Index Terms

Natural language processing of lyrics
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Music retrieval

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Published In

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

November 2005

1110 pages

ISBN:1595930442

DOI:10.1145/1101149

General Chairs:
Hongjiang Zhang
Microsoft Research Asia, China
,
Tat-Seng Chua
National University of Singapore, Singapore
,
Program Chairs:
Ralf Steinmetz
Technische Universitat Darmstadt, Germany
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Lynn Wilcox
FXPAL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 06 November 2005

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MM05: 2005 13th Annual ACM International Conference on Multimedia

November 6 - 11, 2005

Hilton, Singapore

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Jasmitha TMeeradevi J S(2024)Artificial Intelligence for Lyric Generator based on Bi-Directional LSTM and Transformers2024 5th International Conference on Circuits, Control, Communication and Computing (I4C)10.1109/I4C62240.2024.10748468(228-234)Online publication date: 4-Oct-2024
https://doi.org/10.1109/I4C62240.2024.10748468
Casanovas-Buliart LAlvarez-Cueva PCastillo C(2024)Evolution over 62 years: an analysis of sexism in the lyrics of the most-listened-to songs in SpainCogent Arts & Humanities10.1080/23311983.2024.243672311:1Online publication date: 11-Dec-2024
https://doi.org/10.1080/23311983.2024.2436723
Khamis MGomaa W(2024)Analysis of the Arab singer Shadia’s lyricsInternational Journal of Data Science and Analytics10.1007/s41060-024-00669-9Online publication date: 9-Nov-2024
https://doi.org/10.1007/s41060-024-00669-9
Baruah NGoutom PKalita AGogoi ABezbaruah MPrasad NDas VPhukan R(2024)Detection of Explicit Lyrics in Hindi Music Using Different Machine Learning AlgorithmsAdvances in Data-Driven Computing and Intelligent Systems10.1007/978-981-99-9524-0_4(33-45)Online publication date: 26-Feb-2024
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WATANABE KGOTO M(2023)A Method to Detect Chorus Sections in Lyrics TextIEICE Transactions on Information and Systems10.1587/transinf.2022EDP7139E106.D:9(1600-1609)Online publication date: 1-Sep-2023
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Baruah NKalita AGogoi ABezbaruah MPrasad NDas VPhukan R(2023)Detection of Explicit Lyrics in Hindi Music Using LSTM2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308139(1-5)Online publication date: 6-Jul-2023
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Dhandapani AIlakiyaselvan NMandal SBhadra SViswanathan V(2023)Lyrics Generation Using LSTM and RNNBig Data and Cloud Computing10.1007/978-981-99-1051-9_24(371-388)Online publication date: 11-Jun-2023
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Fell MCabrio ETikat MMichel FBuffa MGandon F(2022)The WASABI song corpus and knowledge graph for music lyrics analysisLanguage Resources and Evaluation10.1007/s10579-022-09601-857:1(89-119)Online publication date: 21-Jul-2022
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Egivenia ERyanie Setiawan GShania Mintara SSuhartono D(2021)Classification of Explicit Music Content Based on Lyrics, Music Metadata, and User AnnotationProceedings of the 6th International Conference on Sustainable Information Engineering and Technology10.1145/3479645.3479695(265-270)Online publication date: 13-Sep-2021
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Renault LVaglio AHennequin R(2021)Singing Language Identification Using a Deep Phonotactic ApproachICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP39728.2021.9414203(271-275)Online publication date: 6-Jun-2021
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Emotion-Based Music Information Retrieval Using Lyrics

LAMP, A Lyrics and Audio MandoPop Dataset for Music Mood Estimation: Dataset Compilation, System Construction, and Testing

Combination of audio and lyrics features for genre classification in digital audio collections

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