{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:53:20Z","timestamp":1771656800515,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J AUDIO SPEECH MUSIC PROC."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Most music listeners have an intuitive understanding of the notion of rhythm complexity. Musicologists and scientists, however, have long sought objective ways to measure and model such a distinctively perceptual attribute of music. Whereas previous research has mainly focused on monophonic patterns, this article presents a novel perceptually-informed rhythm complexity measure specifically designed for polyphonic rhythms, i.e., patterns in which multiple simultaneous voices cooperate toward creating a coherent musical phrase. We focus on drum rhythms relating to the Western musical tradition and validate the proposed measure through a perceptual test where users were asked to rate the complexity of real-life drumming performances. Hence, we propose a latent vector model for rhythm complexity based on a recurrent variational autoencoder tasked with learning the complexity of input samples and embedding it along one latent dimension. Aided by an auxiliary adversarial loss term promoting disentanglement, this effectively regularizes the latent space, thus enabling explicit control over the complexity of newly generated patterns. Trained on a large corpus of MIDI files of polyphonic drum recordings, the proposed method proved capable of generating coherent and realistic samples at the desired complexity value. In our experiments, output and target complexities show a high correlation, and the latent space appears interpretable and continuously navigable. On the one hand, this model can readily contribute to a wide range of creative applications, including, for instance, assisted music composition and automatic music generation. On the other hand, it brings us one step closer toward achieving the ambitious goal of equipping machines with a human-like understanding of perceptual features of music.<\/jats:p>","DOI":"10.1186\/s13636-022-00267-2","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T11:06:13Z","timestamp":1676631973000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A latent rhythm complexity model for attribute-controlled drum pattern generation"],"prefix":"10.1186","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9438-3190","authenticated-orcid":false,"given":"Alessandro Ilic","family":"Mezza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Massimiliano","family":"Zanoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Augusto","family":"Sarti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"issue":"3","key":"267_CR1","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1080\/09298215.2016.1200631","volume":"45","author":"A Flexer","year":"2016","unstructured":"A. Flexer, T. Grill, The problem of limited inter-rater agreement in modelling music similarity. J. New Music. Res. 45(3), 239\u2013251 (2016)","journal-title":"J. New Music. Res."},{"key":"267_CR2","unstructured":"M. Sordo, \u00d2. Celma, M. Blech, E. Guaus, in Proc. of the 9th International Conference on Music Information Retrieval,\u00a0Philadelphia,\u00a0 2008. The quest for musical genres: do the experts and the wisdom of crowds agree? (2008), p. 255\u2013260"},{"key":"267_CR3","doi-asserted-by":"crossref","unstructured":"S. Yang, C.N. Reed, E. Chew, M. Barthet, Examining emotion perception agreement in live music performance. IEEE Trans. Affect. Comput. (2021).\u00a0https:\/\/ieeexplore.ieee.org\/document\/9468946\/","DOI":"10.1109\/TAFFC.2021.3093787"},{"issue":"4","key":"267_CR4","doi-asserted-by":"publisher","first-page":"483","DOI":"10.3758\/BF03337859","volume":"5","author":"JL Walker","year":"1977","unstructured":"J.L. Walker, Subjective reactions to music and brainwave rhythms. Physiol. Psychol. 5(4), 483\u2013489 (1977)","journal-title":"Physiol. Psychol."},{"key":"267_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpsyg.2016.00069","volume":"7","author":"TE Matthews","year":"2016","unstructured":"T.E. Matthews, J.N.L. Thibodeau, B.P. Gunther, V.B. Penhune, The impact of instrument-specific musical training on rhythm perception and production. Front. Psychol. 7, 1\u201316 (2016)","journal-title":"Front. Psychol."},{"key":"267_CR6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/S0079-6123(09)17805-6","volume":"178","author":"SJ Morrison","year":"2009","unstructured":"S.J. Morrison, S.M. Demorest, Cultural constraints on music perception and cognition. Prog. Brain Res. 178, 67\u201377 (2009)","journal-title":"Prog. Brain Res."},{"key":"267_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0034102","volume-title":"Music, Gestalt, and Computing: Studies in Cognitive and Systematic Musicology","author":"M Leman","year":"1997","unstructured":"M. Leman, Music, Gestalt, and Computing: Studies in Cognitive and Systematic Musicology (Springer, Berlin-Heidelberg, 1997)"},{"issue":"4","key":"267_CR8","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/40285271","volume":"1","author":"HC Longuet-Higgins","year":"1984","unstructured":"H.C. Longuet-Higgins, C.S. Lee, The rhythmic interpretation of monophonic music. Music. Percept. 1(4), 424\u2013441 (1984)","journal-title":"Music. Percept."},{"issue":"4","key":"267_CR9","doi-asserted-by":"publisher","first-page":"411","DOI":"10.2307\/40285311","volume":"2","author":"D-J Povel","year":"1985","unstructured":"D.-J. Povel, P. Essens, Perception of temporal patterns. Music. Percept. 2(4), 411\u2013440 (1985)","journal-title":"Music. Percept."},{"key":"267_CR10","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511518317","volume-title":"African Polyphony and Polyrhythm: Musical Structure and Methodology","author":"S Arom","year":"1991","unstructured":"S. Arom, G. Ligeti, African Polyphony and Polyrhythm: Musical Structure and Methodology (Cambridge University Press, Cambridge, 1991)"},{"issue":"4","key":"267_CR11","doi-asserted-by":"publisher","first-page":"465","DOI":"10.2307\/40285634","volume":"11","author":"AS Tanguiane","year":"1994","unstructured":"A.S. Tanguiane, A principle of correlativity of perception and its application to music recognition. Music. Percept. Interdiscip. J. 11(4), 465\u2013502 (1994)","journal-title":"Music. Percept. Interdiscip. J."},{"issue":"1","key":"267_CR12","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1002629217152","volume":"35","author":"I Shmulevich","year":"2001","unstructured":"I. Shmulevich, O. Yli-Harja, E. Coyle, D.-J. Povel, K. Lemstr\u00f6m, Perceptual issues in music pattern recognition: complexity of rhythm and key finding. Comput. Hum. 35(1), 23\u201335 (2001)","journal-title":"Comput. Hum."},{"key":"267_CR13","unstructured":"G. Toussaint, in Bridges: Mathematical Connections in Art, Music, and Science, Towson, 2002. A mathematical analysis of african, brazilian, and cuban clave rhythms. (Bridges Conference, Winfield, 2002), p. 157\u2013168"},{"key":"267_CR14","unstructured":"L.M. Smith, H. Honing, in Proc. of the 2006 International Computer Music Conference, New Orleans, 2006. Evaluating and extending computational models of rhythmic syncopation in music. (Michigan Publishing, Ann Arbor, 2006), p. 688\u2013691"},{"issue":"1","key":"267_CR15","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1525\/mp.2007.25.1.43","volume":"25","author":"WT Fitch","year":"2007","unstructured":"W.T. Fitch, A.J. Rosenfeld, Perception and production of syncopated rhythms. Music. Percept. 25(1), 43\u201358 (2007)","journal-title":"Music. Percept."},{"key":"267_CR16","unstructured":"G.T. Toussaint, in Proc. of the 12th International Conference on Music Perception and Cognition & the 8th Conference of the European Society for the Cognitive Sciences of Music, Thessaloniki, 2012. The pairwise variability index as a tool in musical rhythm analysis. (School of Music Studies, Aristotle University of Thessaloniki, Thessaloniki, 2012), p. 1001\u20131008"},{"key":"267_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpsyg.2014.01111","volume":"5","author":"P Vuust","year":"2014","unstructured":"P. Vuust, M.A.G. Witek, Rhythmic complexity and predictive coding: a novel approach to modeling rhythm and meter perception in music. Front. Psychol. 5, 1\u201314 (2014)","journal-title":"Front. Psychol."},{"key":"267_CR18","unstructured":"A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, K. Kavukcuoglu, in Proc. of the 30th International Conference on Neural Information Processing Systems, Barcelona, 2016. Conditional image generation with PixelCNN decoders. (Curran Associates Inc., Red Hook, 2016), p. 4797\u20134805"},{"key":"267_CR19","doi-asserted-by":"crossref","unstructured":"C. Ledig, L. Theis, F. Husz\u00e1r, J. Caballero, A.P. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, in Proc. of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017. Photo-realistic single image super-resolution using a generative adversarial network. (IEEE, Piscataway, 2017), p. 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"key":"267_CR20","unstructured":"A. Razavi, A. van den Oord, O. Vinyals, in Proc. of the 33rd International Conference on Neural Information Processing Systems, Vancouver, 2019. Generating diverse high-fidelity images with VQ-VAE-2. (Curran Associates Inc., Red Hook, 2019), p. 1\u201311"},{"key":"267_CR21","doi-asserted-by":"crossref","unstructured":"S.R. Bowman, L. Vilnis, O. Vinyals, A. Dai, R. Jozefowicz, S. Bengio, in Proc. of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, 2016. Generating sentences from a continuous space. (Association for Computational Linguistics, Stroudsburg, 2016), p. 10\u201321","DOI":"10.18653\/v1\/K16-1002"},{"key":"267_CR22","doi-asserted-by":"crossref","unstructured":"Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le, R. Salakhutdinov, in Proc. of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, 2019. Transformer-XL: Attentive language models beyond a fixed-length context. (Association for Computational Linguistics, Stroudsburg, 2019), p. 2978\u20132988","DOI":"10.18653\/v1\/P19-1285"},{"key":"267_CR23","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"T. Brown et al., Language models are few-shot learners. Adv Neural Inf Proc Syst 33, 1877\u20131901 (2020)","journal-title":"Adv Neural Inf Proc Syst"},{"key":"267_CR24","unstructured":"A. van\u00a0der\u00a0Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, K. Kavukcuoglu, Wavenet: a generative model for raw audio. (2016). arXiv:1609.03499"},{"key":"267_CR25","doi-asserted-by":"crossref","unstructured":"W.-N. Hsu, Y. Zhang, J. Glass, in Proc. of the 18th Annual Conference of the International Speech Communication Association, Stockholm, 2017. Learning latent representations for speech generation and transformation. (Curran Associates Inc., Red Hook, 2017), p. 1273\u20131277","DOI":"10.21437\/Interspeech.2017-349"},{"key":"267_CR26","doi-asserted-by":"crossref","unstructured":"K. Akuzawa, Y. Iwasawa, Y. Matsuo, in Proc. of the 19th Annual Conference of the International Speech Communication Association, Hyderabad, 2018. Expressive speech synthesis via modeling expressions with variational autoencoder. (Curran Associates Inc., Red Hook, 2018), p. 3067\u20133071","DOI":"10.21437\/Interspeech.2018-1113"},{"issue":"4","key":"267_CR27","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1007\/s00521-018-3758-9","volume":"32","author":"S Oore","year":"2020","unstructured":"S. Oore, I. Simon, S. Dieleman, D. Eck, K. Simonyan, This time with feeling: learning expressive musical performance. Neural Comput. Applic. 32(4), 955\u2013967 (2020)","journal-title":"Neural Comput. Applic."},{"key":"267_CR28","unstructured":"A. Roberts, J. Engel, S. Oore, D. Eck, Learning latent representations of music to generate interactive musical palettes. Paper presented at the 2018 ACM Workshop on Intelligent Music Interfaces for Listening and Creation, Tokyo, 2018."},{"key":"267_CR29","unstructured":"A. Roberts, J. Engel, C. Raffel, C. Hawthorne, D. Eck, in Proc. of the 35th International Conference on Machine Learning, Stockholm, 2018. A hierarchical latent vector model for learning long-term structure in music, vol. 80. (Curran Associates Inc., Red Hook, 2018), p. 4364\u20134373"},{"key":"267_CR30","unstructured":"C.-Z.A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, I. Simon, C. Hawthorne, A. Dai, M. Hoffman, M. Dinculescu, D. Eck, Music transformer: Generating music with long-term structure. (2019). arXiv:1809.04281"},{"key":"267_CR31","unstructured":"J. Gillick, A. Roberts, J. Engel, D. Eck, D. Bamman, in Proc. of the 36th International Conference on Machine Learning, Long Beach, 2019. Learning to groove with inverse sequence transformations, vol. 97. (Curran Associates Inc., Red Hook, 2019), p. 2269\u20132279"},{"key":"267_CR32","unstructured":"P. Dhariwal, H. Jun, C. Payne, J.W. Kim, A. Radford, I. Sutskever, Jukebox: a generative model for music. (2020). arXiv:2005.00341"},{"issue":"4","key":"267_CR33","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1007\/s00521-018-3813-6","volume":"32","author":"J-P Briot","year":"2020","unstructured":"J.-P. Briot, F. Pachet, Deep learning for music generation: challenges and directions. Neural Comput. Applic. 32(4), 981\u2013993 (2020)","journal-title":"Neural Comput. Applic."},{"key":"267_CR34","unstructured":"J.H. Engel, M.D. Hoffman, A. Roberts, Latent constraints: learning to generate conditionally from unconditional generative models. Paper presented at the 5th International Conference on Learning Representations, Toulon, 2017."},{"key":"267_CR35","doi-asserted-by":"crossref","unstructured":"G. Hadjeres, F. Nielsen, F. Pachet, in 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, 2017.GLSR-VAE: geodesic latent space regularization for variational autoencoder architectures. (Curran Associates Inc., Red Hook, 2017), p. 1\u20137","DOI":"10.1109\/SSCI.2017.8280895"},{"key":"267_CR36","unstructured":"G. Brunner, A. Konrad, Y. Wang, R. Wattenhofer, in Proc. of the 19th International Society for Music Information Retrieval Conference, Paris, 2018. MIDI-VAE: modeling dynamics and instrumentation of music with applications to style transfer. (2018), p. 747\u2013754"},{"key":"267_CR37","unstructured":"H.H. Tan, D. Herremans, in Proc. of the 21st International Society for Music Information Retrieval Conference, Montr\u00e9al, 2020. Music fadernets: controllable music generation based on high-level features via low-level feature modelling. (2020), p. 109\u2013116"},{"key":"267_CR38","unstructured":"G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer, M. Ranzato, Fader networks: manipulating images by sliding attributes.\u00a0Adv. Neural. Inf. Proc. Syst.\u00a030, 5969\u20135978 (2017)"},{"key":"267_CR39","doi-asserted-by":"crossref","unstructured":"Z. Jiang, Y. Zheng, H. Tan, B. Tang, H. Zhou, in Proc. of the 26th International Joint Conference on Artificial Intelligence, Melbourne, 2017. Variational deep embedding: an unsupervised and generative approach to clustering. (AAAI Press, Cambridge, 2017), p. 1965\u201372","DOI":"10.24963\/ijcai.2017\/273"},{"issue":"9","key":"267_CR40","doi-asserted-by":"publisher","first-page":"4429","DOI":"10.1007\/s00521-020-05270-2","volume":"33","author":"A Pati","year":"2021","unstructured":"A. Pati, A. Lerch, Attribute-based regularization of latent spaces for variational auto-encoders. Neural Comput. Applic. 33(9), 4429\u20134444 (2021)","journal-title":"Neural Comput. Applic."},{"key":"267_CR41","unstructured":"A. Pati, A. Lerch, in Proc. of the 22nd International Society for Music Information Retrieval Conference, Online, 2021. Is disentanglement enough? On latent representations for controllable music generation. (2021), p. 517\u2013524"},{"key":"267_CR42","unstructured":"F. G\u00f3mez, A. Melvin, D. Rappaport, G.T. Toussaint, in Renaissance Banff: Mathematics, Music, Art, Culture, Banff, 2005. Mathematical measures of syncopation. (Canadian Mathematical Society, The Banff Centre, PIMS, 2005), p. 73\u201384"},{"key":"267_CR43","unstructured":"F. G\u00f3mez, E. Thul, G. Toussaint, in Proc. of the 2007 International Computer Music Conference, Copenhagen, 2017. An experimental comparison of formal measures of rhythmic syncopation. (Michigan Publishing, Ann Arbor, 2007), p. 101\u2013104"},{"key":"267_CR44","unstructured":"E. Thul, G.T. Toussaint, in Proc. of the 9th International Society for Music Information Retrieval Conference, Philadelphia, 2008. Rhythm complexity measures: a comparison of mathematical models of human perception and performance. (2008), p. 663\u2013668"},{"key":"267_CR45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21945-5","volume-title":"Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications","author":"M M\u00fcller","year":"2015","unstructured":"M. M\u00fcller, Fundamentals of music processing: audio, analysis, algorithms, applications (Springer, Basel, 2015)"},{"key":"267_CR46","unstructured":"J. Bilmes, in Proc. of the 1993 International Computer Music Conference, Tokyo, 1993. Techniques to foster drum machine expressivity. (Michigan Publishing, Ann Arbor, 1993), p. 276\u2013283"},{"key":"267_CR47","volume-title":"From Polychords to P\u00f3lya: Adventures in Musical Combinatorics","author":"M Keith","year":"1991","unstructured":"M. Keith, From Polychords to P\u00f3lya: Adventures in Musical Combinatorics (Vinculum Press, Princeton, 1991)"},{"key":"267_CR48","unstructured":"G. Toussaint, in Meeting Alhambra, ISAMA-BRIDGES Conference Proceedings. Classification and phylogenetic analysis of african ternary rhythm timelines. (2003), p. 25\u201336"},{"issue":"1982","key":"267_CR49","first-page":"515","volume":"7","author":"E Grabe","year":"2002","unstructured":"E. Grabe, E.L. Low, Durational variability in speech and the rhythm class hypothesis. Pap. Lab. Phonol. 7(1982), 515\u2013546 (2002)","journal-title":"Pap. Lab. Phonol."},{"issue":"5","key":"267_CR50","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1037\/h0028113","volume":"76","author":"PC Vitz","year":"1969","unstructured":"P.C. Vitz, T.C. Todd, A coded element model of the perceptual processing of sequential stimuli. Psychol. Rev. 76(5), 433\u2013449 (1969)","journal-title":"Psychol. Rev."},{"issue":"1","key":"267_CR51","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/TIT.1976.1055501","volume":"22","author":"A Lempel","year":"1976","unstructured":"A. Lempel, J. Ziv, On the complexity of finite sequences. IEEE Trans. Inf. Theory. 22(1), 75\u201381 (1976)","journal-title":"IEEE Trans. Inf. Theory."},{"key":"267_CR52","unstructured":"G. Sioros, C. Guedes, in Proc. of the 12th International Society for Music Information Retrieval Conference, Miami, 2011. Complexity driven recombination of MIDI loops. (2011), p. 381\u2013386"},{"key":"267_CR53","unstructured":"G. Sioros, A. Holzapfel, C. Guedes, in Proc. of the 13th International Society for Music Information Retrieval Conference, Porto, 2012. On measuring syncopation to drive an interactive music system. (2012), p. 283\u2013288"},{"key":"267_CR54","unstructured":"Subjective evaluation of speech quality with a crowdsourcing approach. Rec. ITU-T P.808. (International Telecommunication Union, Geneva, 2018)"},{"key":"267_CR55","unstructured":"I. Higgins, L. Matthey, A. Pal, C.P. Burgess, X. Glorot, M.M. Botvinick, S. Mohamed, A. Lerchner, beta-VAE: learning basic visual concepts with a constrained variational framework. Paper presented at the 5th International Conference on Learning Representations, Toulon, 2017."},{"key":"267_CR56","unstructured":"T. Adel, Z. Ghahramani, A. Weller, in Proc. of the 35th International Conference on Machine Learning, Stockholm, 2018. Discovering interpretable representations for both deep generative and discriminative models, vol. 80. (Curran Associates Inc., Red Hook, 2018), p. 50\u201359"},{"issue":"8","key":"267_CR57","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern. Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"267_CR58","unstructured":"C.P. Burgess, I. Higgins, A. Pal, L. Matthey, N. Watters, G. Desjardins, A. Lerchner, Understanding disentangling in $$\\beta$$-VAE. Paper presented at the 2017 NIPS Workshop on Learning Disentangled Representations, Long Beach, 2018."},{"issue":"6","key":"267_CR59","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1162\/neco.1992.4.6.863","volume":"4","author":"J Schmidhuber","year":"1992","unstructured":"J. Schmidhuber, Learning factorial codes by predictability minimization. Neural Comput. 4(6), 863\u2013879 (1992)","journal-title":"Neural Comput."},{"issue":"1","key":"267_CR60","first-page":"2096","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, V. Lempitsky, Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096\u20132030 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"267_CR61","unstructured":"D.P. Kingma, J. Ba, Adam: a method for stochastic optimization. Paper presented at the 3rd International Conference on Learning Representations, San Diego, 2015."},{"key":"267_CR62","unstructured":"C. Eastwood, C.K. Williams, A framework for the quantitative evaluation of disentangled representations. Paper presented at the 6th International Conference on Learning Representations, Vancouver, 2018."},{"key":"267_CR63","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/12513.001.0001","volume-title":"A Generative Theory of Tonal Music, Reissue, with a New Preface","author":"F Lerdahl","year":"1996","unstructured":"F. Lerdahl, R.S. Jackendoff, A Generative Theory of Tonal Music, Reissue, with a New Preface (MIT Press, Cambridge, 1996)"}],"container-title":["EURASIP Journal on Audio, Speech, and Music Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13636-022-00267-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13636-022-00267-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13636-022-00267-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T11:06:43Z","timestamp":1676632003000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13636-022-00267-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["267"],"URL":"https:\/\/doi.org\/10.1186\/s13636-022-00267-2","relation":{},"ISSN":["1687-4722"],"issn-type":[{"value":"1687-4722","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,17]]},"assertion":[{"value":"31 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All participants to the listening experiment gave their consent to take part in the study and no personal data was collected.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11"}}