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The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity

Authors:

Jacopo de Berardinis,

Albert Meroño-Peñuela,

Andrea Poltronieri,

Valentina PresuttiAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 3873 - 3882

https://doi.org/10.1145/3543507.3587428

Published: 30 April 2023 Publication History

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Abstract

Computationally creative systems for music have recently achieved impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions.

References

[1]

Eytan Agmon. 1995. Functional Harmony Revisited: A Prototype-Theoretic Approach. Music Theory Spectrum 17, 2 (10 1995), 196–214. https://doi.org/10.2307/745871 arXiv:https://academic.oup.com/mts/article-pdf/17/2/196/6138625/17-2-196.pdf

[2]

Andrea Agostinelli, Timo I. Denk, Zalán Borsos, Jesse H. Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, Matthew Sharifi, Neil Zeghidour, and Christian Havnø Frank. 2023. MusicLM: Generating Music From Text. CoRR abs/2301.11325 (2023). https://doi.org/10.48550/arXiv.2301.11325

[3]

Greg Aloupis, Thomas Fevens, Stefan Langerman, Tomomi Matsui, Antonio Mesa, Yurai Núñez Rodríguez, David Rappaport, and Godfried T. Toussaint. 2006. Algorithms for Computing Geometric Measures of Melodic Similarity. Comput. Music. J. 30, 3 (2006), 67–76. https://doi.org/10.1162/comj.2006.30.3.67

Digital Library

[4]

Margaret A Boden. 1992. Understanding creativity. The Journal of Creative Behavior (1992).

[5]

Margaret A Boden. 2004. The creative mind: Myths and mechanisms. Routledge.

Digital Library

[6]

Paul M. Bodily and Dan Ventura. 2018. Explainability: An Aesthetic for Aesthetics in Computational Creative Systems. In Proceedings of the Ninth International Conference on Computational Creativity, ICCC 2018, Salamanca, Spain, June 25-29, 2018, François Pachet, Anna Jordanous, and Carlos León (Eds.). Association for Computational Creativity (ACC), 153–160. http://computationalcreativity.net/iccc2018/sites/default/files/papers/ICCC_2018_paper_42.pdf

[7]

Jean-Pierre Briot, Gaëtan Hadjeres, and François-David Pachet. 2020. Deep learning techniques for music generation. Vol. 1. Springer.

[8]

Nick Bryan-Kinns, Berker Banar, Corey Ford, C Reed, Yixiao Zhang, Simon Colton, Jack Armitage, 2022. Exploring xai for the arts: Explaining latent space in generative music. (2022).

[9]

Benjamin Burger, Phillip M Maffettone, Vladimir V Gusev, Catherine M Aitchison, Yang Bai, Xiaoyan Wang, Xiaobo Li, Ben M Alston, Buyi Li, Rob Clowes, 2020. A mobile robotic chemist. Nature 583, 7815 (2020), 237–241.

[10]

Valentina Anita Carriero, Fiorela Ciroku, Jacopo de Berardinis, Delfina Sol Martinez Pandiani, Albert Meroño-Peñuela, Andrea Poltronieri, and Valentina Presutti. 2021. Semantic Integration of MIR Datasets with the Polifonia Ontology Network. In ISMIR Late Breaking Demo.

[11]

Shan Carter and Michael Nielsen. 2017. Using artificial intelligence to augment human intelligence. Distill 2, 12 (2017), e9.

[12]

Nick Collins, V Ruzicka, and Mick Grierson. 2020. Remixing AIs: mind swaps, hybrainity, and splicing musical models. In Proc. The Joint Conference on AI Music Creativity.

[13]

Simon Colton, John William Charnley, and Alison Pease. 2011. Computational Creativity Theory: The FACE and IDEA Descriptive Models. In ICCC. Mexico City, 90–95.

[14]

Simon Colton and Dan Ventura. 2014. You Can’t Know my Mind: A Festival of Computational Creativity. In Proceedings of the Fifth International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia, June 10-13, 2014, Simon Colton, Dan Ventura, Nada Lavrac, and Michael Cook (Eds.). computationalcreativity.net, 351–354. http://computationalcreativity.net/iccc2014/wp-content/uploads/2014/06/15.8_Colton.pdf

[15]

Marco Cuturi and Mathieu Blondel. 2017. Soft-DTW: a Differentiable Loss Function for Time-Series. In Proceedings of the 34th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 894–903. https://proceedings.mlr.press/v70/cuturi17a.html

[16]

Jacopo de Berardinis, Albert Meroño-Peñuela, Andrea Poltronieri, and Valentina Presutti. 2023. ChoCo: a Chord Corpus and a Data Transformation Workflow for Musical Harmony Knowledge Graphs. In Manuscript under review.

[17]

Jacopo de Berardinis, Michalis Vamvakaris, Angelo Cangelosi, and Eduardo Coutinho. 2020. Unveiling the hierarchical structure of music by multi-resolution community detection. Transactions of the International Society for Music Information Retrieval 3, 1 (2020), 82–97.

[18]

W. Bas de Haas, Remco C. Veltkamp, and Frans Wiering. 2008. Tonal Pitch Step Distance: a Similarity Measure for Chord Progressions. In ISMIR 2008, 9th International Conference on Music Information Retrieval, Drexel University, Philadelphia, PA, USA, September 14-18, 2008, Juan Pablo Bello, Elaine Chew, and Douglas Turnbull (Eds.). 51–56. http://ismir2008.ismir.net/papers/ISMIR2008_252.pdf

[19]

W. Bas de Haas, Frans Wiering, and Remco C. Veltkamp. 2013. A geometrical distance measure for determining the similarity of musical harmony. International Journal of Multimedia Information Retrieval 2, 3 (01 09 2013), 189–202. https://doi.org/10.1007/s13735-013-0036-6

[20]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[21]

Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever. 2020. Jukebox: A generative model for music. arXiv preprint arXiv:2005.00341 (2020).

[22]

Eric Drott. 2021. Copyright, compensation, and commons in the music AI industry. Creative Industries Journal 14, 2 (2021), 190–207.

[23]

M. du Sautoy. 2019. The Creativity Code: How AI is learning to write, paint and think. HarperCollins Publishers.

[24]

Wlodzislaw Duch. 2007. Intuition, insight, imagination and creativity. IEEE Computational Intelligence Magazine 2, 3 (2007), 40–52.

Digital Library

[25]

Vsevolod Eremenko, Emir Demirel, Baris Bozkurt, and Xavier Serra. 2018. JAAH: Audio-aligned jazz harmony dataset. https://doi.org/10.5281/zenodo.1290737

[26]

Luciano Floridi. 2019. Establishing the rules for building trustworthy AI. Nature Machine Intelligence 1, 6 (2019), 261–262.

[27]

Jonathan Foote. 1999. Visualizing music and audio using self-similarity. In Proceedings of the seventh ACM international conference on Multimedia (Part 1). 77–80.

Digital Library

[28]

Aldo Gangemi and Peter Mika. 2003. Understanding the Semantic Web through Descriptions and Situations. In On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, Robert Meersman, Zahir Tari, and Douglas C. Schmidt (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 689–706.

[29]

Shaghayegh Gharghabi, Yifei Ding, Chin-Chia Michael Yeh, Kaveh Kamgar, Liudmila Ulanova, and Eamonn Keogh. 2017. Matrix profile VIII: domain agnostic online semantic segmentation at superhuman performance levels. In 2017 IEEE international conference on data mining (ICDM). IEEE, 117–126.

[30]

Zixun Guo, Jaeyong Kang, and Dorien Herremans. 2022. A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling. arXiv preprint arXiv:2212.00973 (2022).

[31]

Christopher Harte, Mark B Sandler, Samer A Abdallah, and Emilia Gómez. 2005. Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations. In ISMIR, Vol. 5. 66–71.

[32]

Cheng-Zhi Anna Huang, Tim Cooijmans, Adam Roberts, Aaron C. Courville, and Douglas Eck. 2017. Counterpoint by Convolution. In Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017, Suzhou, China, October 23-27, 2017, Sally Jo Cunningham, Zhiyao Duan, Xiao Hu, and Douglas Turnbull (Eds.). 211–218.

[33]

Cheng-Zhi Anna Huang, Hendrik Vincent Koops, Ed Newton-Rex, Monica Dinculescu, and Carrie J Cai. 2020. AI song contest: Human-AI co-creation in songwriting. arXiv preprint arXiv:2010.05388 (2020).

[34]

Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M Dai, Matthew D Hoffman, Monica Dinculescu, and Douglas Eck. 2018. Music transformer. arXiv preprint arXiv:1809.04281 (2018).

[35]

Jaehun Kim, Julián Urbano, Cynthia Liem, and Alan Hanjalic. 2020. One Deep Music Representation to Rule Them All¿: A Comparative Analysis of Different Representation Learning Strategies. Neural Computing and Applications 32, 4 (2020), 1067–1093.

Digital Library

[36]

Arto Klami, Theodoros Damoulas, Ola Engkvist, Patrick Rinke, and Samuel Kaski. 2022. Virtual Laboratories: Transforming research with AI. (2022).

[37]

Stefan Koelsch. 2011. Toward a neural basis of music perception–a review and updated model. Frontier in Psychology 2 (2011), 110.

[38]

Hendrik Vincent Koops, W. Bas de Haas, John Ashley Burgoyne, Jeroen Bransen, Anna Kent-Muller, and Anja Volk. 2019. Annotator subjectivity in harmony annotations of popular music. Journal of New Music Research 48, 3 (2019), 232–252. https://doi.org/10.1080/09298215.2019.1613436

[39]

Allison Lahnala, Gauri Kambhatla, Jiajun Peng, Matthew Whitehead, Gillian Minnehan, Eric Guldan, Jonathan K. Kummerfeld, Anil Çamci, and Rada Mihalcea. 2021. Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction. In Artificial Intelligence in Music, Sound, Art and Design - 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings(Lecture Notes in Computer Science, Vol. 12693), Juan Romero, Tiago Martins, and Nereida Rodríguez-Fernández (Eds.). Springer, 171–186. https://doi.org/10.1007/978-3-030-72914-1_12

Digital Library

[40]

Agnieszka Lawrynowicz. 2020. Creative AI: A new avenue for the Semantic Web¿Semantic Web 11 (2020), 69–78. https://doi.org/10.3233/SW-190377 1.

Digital Library

[41]

Nicolas Lazzari, Andrea Poltronieri, and Valentina Presutti. 2022. Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings. In Proceedings of the 1st Workshop on Artificial Intelligence and Creativity co-located with 21th International Conference of the Italian Association for Artificial Intelligence(AIxIA 2022), Udine, Italy, November 28 - December 3, 2022(CEUR Workshop Proceedings, Vol. 3278), Allegra De Filippo, Michela Milano, Valentina Presutti, and Alessandro Saffiotti (Eds.). CEUR-WS.org, 14–30. http://ceur-ws.org/Vol-3278/paper2.pdf

[42]

Fred Lerdahl. 1988. Tonal Pitch Space. Music Perception: An Interdisciplinary Journal 5, 3 (1988), 315–349. http://www.jstor.org/stable/40285402

[43]

Fred Lerdahl and Ray Jackendoff. 1983. A generative theory of tonal music. The MIT Press, Cambridge. MA.

[44]

Maria Teresa Llano, Mark d’Inverno, Matthew Yee-King, Jon McCormack, Alon Ilsar, Alison Pease, and Simon Colton. 2020. Explainable Computational Creativity. In Proceedings of the Eleventh International Conference on Computational Creativity, ICCC 2020, Coimbra, Portugal, September 7-11, 2020, F. Amílcar Cardoso, Penousal Machado, Tony Veale, and João Miguel Cunha (Eds.). Association for Computational Creativity (ACC), 334–341. http://computationalcreativity.net/iccc20/papers/067-iccc20.pdf

[45]

Matthias Mauch, Simon Dixon, Christopher Harte, 2007. Discovering chord idioms through Beatles and Real Book songs.

[46]

Rick Meerwaldt, Albert Meroño-Peñuela, and Stefan Schlobach. 2017. Mixing Music as Linked Data: SPARQL-based MIDI Mashups. In WHiSe@ ISWC. 87–98.

[47]

Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1301.3781

[48]

Fabio Morreale. 2021. Where Does the Buck Stop¿ Ethical and Political Issues with AI in Music Creation. Transactions of the International Society for Music Information Retrieval (Jul 2021).

[49]

Lindasalwa Muda, Mumtaj Begam, and I. Elamvazuthi. 2010. Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques. CoRR abs/1003.4083 (2010). http://dblp.uni-trier.de/db/journals/corr/corr1003.html#abs-1003-4083

[50]

Meinard Müller. 2021. Fundamentals of music processing: Using Python and Jupyter notebooks. Vol. 2. Springer.

[51]

Oriol Nieto and Juan Pablo Bello. 2016. Systematic Exploration of Computational Music Structure Research. In Proceedings of the 17th International Society for Music Information Retrieval Conference. ISMIR, New York City, United States, 547–553. https://doi.org/10.5281/zenodo.1417661

[52]

Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, 2022. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155 (2022).

[53]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). ACL, 1532–1543. https://doi.org/10.3115/v1/d14-1162

[54]

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, 2227–2237. https://doi.org/10.18653/v1/N18-1202

[55]

Azzurra Pini, Jer Hayes, Connor Upton, and Medb Corcoran. 2019. AI Inspired Recipes: Designing Computationally Creative Food Combos. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI EA ’19). Association for Computing Machinery, New York, NY, USA, 1–6. https://doi.org/10.1145/3290607.3312948

Digital Library

[56]

Valentina Presutti, Enrico Daga, Aldo Gangemi, and Eva Blomqvist. 2009. eXtreme Design with Content Ontology Design Patterns. In Proceedings of the Workshop on Ontology Patterns (WOP 2009), collocated with the 8th International Semantic Web Conference (ISWC-2009), Washington D.C., USA, 25 October, 2009(CEUR Workshop Proceedings, Vol. 516), Eva Blomqvist, Kurt Sandkuhl, François Scharffe, and Vojtech Svátek (Eds.). CEUR-WS.org. http://ceur-ws.org/Vol-516/pap21.pdf

[57]

Alexander M Putman and Robert M Keller. 2015. A transformational grammar framework for improvisation. In First International Conference on New Music Concepts.

[58]

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In International Conference on Machine Learning. PMLR, 8821–8831.

[59]

Yves Raymond, Samer Abdallah, Mark Sandler, and Frederick Giasson. 2007. The Music Ontology. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007). Vienna, Austria, 417–422.

[60]

Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, and Douglas Eck. 2018. A hierarchical latent vector model for learning long-term structure in music. In International conference on machine learning. PMLR, 4364–4373.

[61]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10684–10695.

[62]

Amaia Salvador, Michal Drozdzal, Xavier Giró-i Nieto, and Adriana Romero. 2019. Inverse cooking: Recipe generation from food images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10453–10462.

[63]

KC Santosh, Bart Lamiroy, and Laurent Wendling. 2013. DTW–Radon-based shape descriptor for pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence 27, 03 (2013), 1350008.

[64]

Bob Sturm, Joao Felipe Santos, and Iryna Korshunova. 2015. Folk music style modelling by recurrent neural networks with long short term memory units. In 16th international society for music information retrieval conference.

[65]

Bob LT Sturm, Maria Iglesias, Oded Ben-Tal, Marius Miron, and Emilia Gómez. 2019. Artificial intelligence and music: open questions of copyright law and engineering praxis. In Arts, Vol. 8. MDPI, 115.

[66]

Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, and Ola Engkvist. 2022. Human-in-the-loop assisted de novo molecular design. Journal of Cheminformatics 14, 1 (2022), 1–16.

[67]

Peter M Todd and Gregory M Werner. 1999. Frankensteinian methods for evolutionary music composition. Musical networks: Parallel distributed perception and performance 3, 4 (1999), 7.

[68]

M. Vlachos, G. Kollios, and D. Gunopulos. 2002. Discovering similar multidimensional trajectories. In Proceedings 18th International Conference on Data Engineering. 673–684. https://doi.org/10.1109/ICDE.2002.994784

[69]

Christof Weiß, Frank Zalkow, Vlora Arifi-Müller, Meinard Müller, Hendrik Vincent Koops, Anja Volk, and Harald G Grohganz. 2021. Schubert Winterreise dataset: A multimodal scenario for music analysis. Journal on Computing and Cultural Heritage (JOCCH) 14, 2 (2021), 1–18.

Digital Library

[70]

Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. 2016. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM). Ieee, 1317–1322.

[71]

Yuan Yuan, Yi-Ping Phoebe Chen, Shengyu Ni, Augix Guohua Xu, Lin Tang, Martin Vingron, Mehmet Somel, and Philipp Khaitovich. 2011. Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series. BMC bioinformatics 12 (2011), 1–13.

Cited By

de Berardinis JMeroño-Peñuela APoltronieri APresutti V(2023)ChoCo: a Chord Corpus and a Data Transformation Workflow for Musical Harmony Knowledge GraphsScientific Data10.1038/s41597-023-02410-w10:1Online publication date: 20-Sep-2023
https://doi.org/10.1038/s41597-023-02410-w
de Berardinis JCarriero VJain NLazzari NMeroño-Peñuela APoltronieri APresutti V(2023)The Polifonia Ontology Network: Building a Semantic Backbone for Musical HeritageThe Semantic Web – ISWC 202310.1007/978-3-031-47243-5_17(302-322)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47243-5_17
Poltronieri A(2023)Knowledge-Based Multimodal Music SimilarityThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_41(224-233)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-43458-7_41

Index Terms

The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Ontology engineering

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WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

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ISBN:9781450394161

DOI:10.1145/3543507

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Cited By

de Berardinis JMeroño-Peñuela APoltronieri APresutti V(2023)ChoCo: a Chord Corpus and a Data Transformation Workflow for Musical Harmony Knowledge GraphsScientific Data10.1038/s41597-023-02410-w10:1Online publication date: 20-Sep-2023
https://doi.org/10.1038/s41597-023-02410-w
de Berardinis JCarriero VJain NLazzari NMeroño-Peñuela APoltronieri APresutti V(2023)The Polifonia Ontology Network: Building a Semantic Backbone for Musical HeritageThe Semantic Web – ISWC 202310.1007/978-3-031-47243-5_17(302-322)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47243-5_17
Poltronieri A(2023)Knowledge-Based Multimodal Music SimilarityThe Semantic Web: ESWC 2023 Satellite Events10.1007/978-3-031-43458-7_41(224-233)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-43458-7_41

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