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Multimodal models are critical for music understanding tasks, as they capture the complex interplay between audio and lyrics.
“Songs and emotions: are lyrics and melodies equal partners?,”
S Omar Ali and Zehra F Peynircioğlu, · 2006
Earlier work this paper cites.
“"why should I trust you?": Explaining the predictions of any classifier,”
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin, · 2016
Earlier work this paper cites.
“Audio set: An ontology and human-labeled dataset for audio events,”
Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R. Channing Moore, Manoj Plakal, and Marvin Ritter, · 2017
Earlier work this paper cites.
“Predicting listener’s mood based on music genre: an adapted reproduced model of russell and thayer,”
Chijioke Worlu, · 2017
Earlier work this paper cites.
“Music mood detection based on audio and lyrics with deep neural net,”
Rémi Delbouys, Romain Hennequin, Francesco Piccoli, Jimena Royo-Letelier, and Manuel Moussallam, · 2018
Earlier work this paper cites.
“Multimodal music information processing and retrieval: Survey and future challenges,”
Federico Simonetta, Stavros Ntalampiras, and Federico Avanzini, · 2019
Earlier work this paper cites.
“Toward interpretable music tagging with self-attention,”
Minz Won, Sanghyuk Chun, and Xavier Serra, · 2019
Earlier work this paper cites.
“Roberta: A robustly optimized bert pretraining approach,” 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov, · 2019
Earlier work this paper cites.
“Global aggregations of local explanations for black box models,”
Ilse van der Linden, Hinda Haned, and Evangelos Kanoulas, · 2019
Earlier work this paper cites.
“Automatic emotion-based music classification for supporting intelligent iot applications,”
Yeong-Seok Seo and Jun-Ho Huh, · 2019
Earlier work this paper cites.
“audiolime: Listenable explanations using source separation,” 2020
Verena Haunschmid, Ethan Manilow, and Gerhard Widmer, · 2020
Earlier work this paper cites.
“Music4all: A new music database and its applications,”
Igor André Pegoraro Santana, Fabio Pinhelli, Juliano Donini, Leonardo Catharin, Rafael Biazus Mangolin, Yandre Maldonado e Gomes da Costa, Valéria Delisandra Feltrim, and Marcos Aurélio Domingues, · 2020
Cited alongside, same era.
“Joyful for you and tender for us: the influence of individual characteristics and language on emotion labeling and classification,”
JS Gómez-Cañón, E Cano, P Herrera, and E Gómez, · 2020
Cited alongside, same era.
“Music emotion detection using weighted of audio and lyric features,”
Fika Hastarita Rachman, Riyanarto Sarno, and Chastine Fatichah, · 2020
Cited alongside, same era.
“AST: audio spectrogram transformer,”
Yuan Gong, Yu-An Chung, and James R. Glass, · 2021
Cited alongside, same era.
“Using machine learning analysis to interpret the relationship between music emotion and lyric features,”
Liang Xu, Zaoyi Sun, Xin Wen, Zhengxi Huang, Chi-ju Chao, and Liuchang Xu, · 2021
“Searching for explanations of black-box classifiers in the space of semantic queries,”
Jason Liartis, Edmund Dervakos, Orfeas Menis-Mastromichalakis, Alexandros Chortaras, and Giorgos Stamou, · 2023
Later among the works it cites.
“Co-design of human-centered, explainable ai for clinical decision support,”
Cecilia Panigutti, Andrea Beretta, Daniele Fadda, Fosca Giannotti, Dino Pedreschi, Alan Perotti, and Salvatore Rinzivillo, · 2023
Later among the works it cites.
“Multimodal explainable artificial intelligence: A comprehensive review of methodological advances and future research directions,” 2023
Nikolaos Rodis, Christos Sardianos, Georgios Th. Papadopoulos, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, and Iraklis Varlamis, · 2023
Later among the works it cites.
“Interpretable multimodal sentiment classification using deep multi-view attentive network of image and text data,”
Israa Khalaf Salman Al-Tameemi, Mohammad-Reza Feizi-Derakhshi, Saeid Pashazadeh, and Mohammad Asadpour, · 2023
Later among the works it cites.
“Hybrid transformers for music source separation,”
Simon Rouard, Francisco Massa, and Alexandre Défossez, · 2023
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Cited alongside, same era.
“Mulan: A joint embedding of music audio and natural language,” 2022
Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, and Daniel P. W. Ellis, · 2022
Cited alongside, same era.
“A model you can hear: Audio identification with playable prototypes,”
Romain Loiseau, Baptiste Bouvier, Yann Teytaut, Elliot Vincent, Mathieu Aubry, and Loïc Landrieu, · 2022
Cited alongside, same era.
“Concept-based techniques for" musicologist-friendly" explanations in a deep music classifier,”
Francesco Foscarin, Katharina Hoedt, Verena Praher, Arthur Flexer, and Gerhard Widmer, · 2022
Cited alongside, same era.
“A survey of transformer-based multimodal pre-trained modals,”
Xue Han, Yi-Tong Wang, Jun-Lan Feng, Chao Deng, Zhan-Heng Chen, Yu-An Huang, Hui Su, Lun Hu, and Peng-Wei Hu, · 2023
Cited alongside, same era.
“Multi-modality in music: Predicting emotion in music from high-level audio features and lyrics,” 2023
Tibor Krols, Yana Nikolova, and Ninell Oldenburg, · 2023
Cited alongside, same era.
“Multimodal music datasets? challenges and future goals in music processing,”
Anna-Maria Christodoulou, Olivier Lartillot, and Alexander Refsum Jensenius,
Cited in the paper.
Later among the works it cites.
“Rule-based explanations of machine learning classifiers using knowledge graphs,”
Orfeas Menis Mastromichalakis, Edmund Dervakos, Alexandros Chortaras, and Giorgos Stamou, · 2024
Closest in time.
“Semantic prototypes: Enhancing transparency without black boxes,”
Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Jason Liartis, Edmund Dervakos, and Giorgos Stamou, · 2024
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“Explainable artificial intelligence: An sts perspective,”
Orfeas Menis Mastromichalakis, · 2024
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“Perceptual musical features for interpretable audio tagging,” 2024
Vassilis Lyberatos, Spyridon Kantarelis, Edmund Dervakos, and Giorgos Stamou, · 2024
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“Leveraging pre-trained autoencoders for interpretable prototype learning of music audio,”
Pablo Alonso-Jiménez, Leonardo Pepino, Roser Batlle-Roca, Pablo Zinemanas, Dmitry Bogdanov, Xavier Serra, and Martín Rocamora, · 2024
Closest in time.