AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions

Diego Vallejo-Huanga; Esteban Carrera; Jonathan Manay

doi:10.1109/ICMLA52953.2021.00254

AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions

Diego Vallejo-Huanga
, Esteban Carrera
, Jonathan Manay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Natural Language Processing (NLP) is one of the most popular fields of Artificial Intelligence and its applications are diverse. This article presents a recommender system, through a web application, that extracts the lyrics of a song entered in audio format and uses NLP techniques to process the textual corpus of the lyrics and recommend a title for the song. The level of effectiveness of the system was analyzed, measuring the percentage of similarity between the ground truth, which is the original title of the song, and our recommendation. For the experiments, a dataset of 30 songs was used divided into three taxonomies, small, medium, and large, that change according to the length of tokens of the original title. The results show an accuracy of 70% for small titles and 20% for medium and long titles. It is also shown that the web tool is enabled to formulate control of lexical content in songs since it does not use the original title of the song as input.

Original language	English
Title of host publication	Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Editors	M. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1588-1593
Number of pages	6
ISBN (Electronic)	9781665443371
DOIs	https://doi.org/10.1109/ICMLA52953.2021.00254
State	Published - 2021
Event	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, United States Duration: 13 Dec 2021 → 16 Dec 2021

Publication series

Name	Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Conference

Conference	20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
Country/Territory	United States
City	Virtual, Online
Period	13/12/21 → 16/12/21

Bibliographical note

Funding Information:
This work was supported by IDEIAGEOCA Research Group of Universidad Politécnica Salesiana in Quito, Ecuador.

Publisher Copyright:
© 2021 IEEE.

Keywords

Content Analysis
LDA
Natural Language Processing
Song Lyrics Extraction.
Speech Recognition

CACES Knowledge Areas

116A Computer Science

Access to Document

10.1109/ICMLA52953.2021.00254

Cite this

Vallejo-Huanga, D., Carrera, E., & Manay, J. (2021). AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions. In M. A. Wani, I. K. Sethi, W. Shi, G. Qu, D. S. Raicu, & R. Jin (Eds.), Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 (pp. 1588-1593). (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA52953.2021.00254

Vallejo-Huanga, Diego ; Carrera, Esteban ; Manay, Jonathan. / AuTGeLy : Automatic Title Generator based on Song Lyrics Extractions. Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. editor / M. Arif Wani ; Ishwar K. Sethi ; Weisong Shi ; Guangzhi Qu ; Daniela Stan Raicu ; Ruoming Jin. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1588-1593 (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021).

@inproceedings{784c2e542aa94354a44c82e1b97e69c5,

title = "AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions",

abstract = "Natural Language Processing (NLP) is one of the most popular fields of Artificial Intelligence and its applications are diverse. This article presents a recommender system, through a web application, that extracts the lyrics of a song entered in audio format and uses NLP techniques to process the textual corpus of the lyrics and recommend a title for the song. The level of effectiveness of the system was analyzed, measuring the percentage of similarity between the ground truth, which is the original title of the song, and our recommendation. For the experiments, a dataset of 30 songs was used divided into three taxonomies, small, medium, and large, that change according to the length of tokens of the original title. The results show an accuracy of 70\% for small titles and 20\% for medium and long titles. It is also shown that the web tool is enabled to formulate control of lexical content in songs since it does not use the original title of the song as input.",

keywords = "Content Analysis, LDA, Natural Language Processing, Song Lyrics Extraction., Speech Recognition",

author = "Diego Vallejo-Huanga and Esteban Carrera and Jonathan Manay",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 ; Conference date: 13-12-2021 Through 16-12-2021",

year = "2021",

doi = "10.1109/ICMLA52953.2021.00254",

language = "English",

series = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1588--1593",

editor = "Wani, \{M. Arif\} and Sethi, \{Ishwar K.\} and Weisong Shi and Guangzhi Qu and Raicu, \{Daniela Stan\} and Ruoming Jin",

booktitle = "Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021",

address = "United States",

}

Vallejo-Huanga, D, Carrera, E & Manay, J 2021, AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions. in MA Wani, IK Sethi, W Shi, G Qu, DS Raicu & R Jin (eds), Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1588-1593, 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021, Virtual, Online, United States, 13/12/21. https://doi.org/10.1109/ICMLA52953.2021.00254

AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions. / Vallejo-Huanga, Diego; Carrera, Esteban; Manay, Jonathan.
Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. ed. / M. Arif Wani; Ishwar K. Sethi; Weisong Shi; Guangzhi Qu; Daniela Stan Raicu; Ruoming Jin. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1588-1593 (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - AuTGeLy

T2 - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

AU - Vallejo-Huanga, Diego

AU - Carrera, Esteban

AU - Manay, Jonathan

PY - 2021

Y1 - 2021

N2 - Natural Language Processing (NLP) is one of the most popular fields of Artificial Intelligence and its applications are diverse. This article presents a recommender system, through a web application, that extracts the lyrics of a song entered in audio format and uses NLP techniques to process the textual corpus of the lyrics and recommend a title for the song. The level of effectiveness of the system was analyzed, measuring the percentage of similarity between the ground truth, which is the original title of the song, and our recommendation. For the experiments, a dataset of 30 songs was used divided into three taxonomies, small, medium, and large, that change according to the length of tokens of the original title. The results show an accuracy of 70% for small titles and 20% for medium and long titles. It is also shown that the web tool is enabled to formulate control of lexical content in songs since it does not use the original title of the song as input.

AB - Natural Language Processing (NLP) is one of the most popular fields of Artificial Intelligence and its applications are diverse. This article presents a recommender system, through a web application, that extracts the lyrics of a song entered in audio format and uses NLP techniques to process the textual corpus of the lyrics and recommend a title for the song. The level of effectiveness of the system was analyzed, measuring the percentage of similarity between the ground truth, which is the original title of the song, and our recommendation. For the experiments, a dataset of 30 songs was used divided into three taxonomies, small, medium, and large, that change according to the length of tokens of the original title. The results show an accuracy of 70% for small titles and 20% for medium and long titles. It is also shown that the web tool is enabled to formulate control of lexical content in songs since it does not use the original title of the song as input.

KW - Content Analysis

KW - LDA

KW - Natural Language Processing

KW - Song Lyrics Extraction.

KW - Speech Recognition

UR - https://www.scopus.com/pages/publications/85125858410

U2 - 10.1109/ICMLA52953.2021.00254

DO - 10.1109/ICMLA52953.2021.00254

M3 - Conference contribution

AN - SCOPUS:85125858410

T3 - Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

SP - 1588

EP - 1593

BT - Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

A2 - Wani, M. Arif

A2 - Sethi, Ishwar K.

A2 - Shi, Weisong

A2 - Qu, Guangzhi

A2 - Raicu, Daniela Stan

A2 - Jin, Ruoming

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 December 2021 through 16 December 2021

ER -

Vallejo-Huanga D, Carrera E, Manay J. AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions. In Wani MA, Sethi IK, Shi W, Qu G, Raicu DS, Jin R, editors, Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1588-1593. (Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021). doi: 10.1109/ICMLA52953.2021.00254

AuTGeLy: Automatic Title Generator based on Song Lyrics Extractions

Abstract

Publication series

Conference

Bibliographical note

Keywords

CACES Knowledge Areas

Access to Document

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Cite this