{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T10:59:16Z","timestamp":1766401156935,"version":"3.37.3"},"reference-count":151,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"UiT The Arctic University of Norway Ph.D. Scholarship Program"},{"name":"Norwegian Government"},{"DOI":"10.13039\/100007465","name":"UiT The Arctic University of Norway","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007465","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3225689","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:23:06Z","timestamp":1669854186000},"page":"125679-125695","source":"Crossref","is-referenced-by-count":8,"title":["Toward Interactive Music Generation: A Position Paper"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1970-5353","authenticated-orcid":false,"given":"Shayan","family":"Dadman","sequence":"first","affiliation":[{"name":"Department of Computer Science, UiT The Arctic University of Norway, Narvik, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernt Arild","family":"Bremdal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UiT The Arctic University of Norway, Narvik, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Borre","family":"Bang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UiT The Arctic University of Norway, Narvik, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6070-303X","authenticated-orcid":false,"given":"Rune","family":"Dalmo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UiT The Arctic University of Norway, Narvik, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"725","article-title":"Structurenet: Inducing structure in generated melodies","author":"medeot","year":"2018","journal-title":"Proc ISMIR"},{"key":"ref38","article-title":"Enhanced memory network: The novel network structure for symbolic music generation","author":"li","year":"2021","journal-title":"arXiv 2110 03392"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2818994"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CONIELECOMP.2018.8327197"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-211-75540-2"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1613\/jair.3908"},{"key":"ref37","article-title":"Text-based LSTM networks for automatic music composition","author":"choi","year":"2016","journal-title":"arXiv 1604 05358"},{"key":"ref36","article-title":"Folk music style modelling by recurrent neural networks with long short term memory units","author":"sturm","year":"2015","journal-title":"Proc 16th Int Soc Music Inf Retr Conf"},{"journal-title":"Learning musical structure directly from sequences of music","year":"2008","author":"eck","key":"ref35"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/NNSP.2002.1030094"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1177\/0305735614543282"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3108242"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1080\/14626268.2018.1426613"},{"key":"ref20","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3505244"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2743240"},{"key":"ref23","article-title":"Deep reinforcement learning: An overview","author":"li","year":"2017","journal-title":"arXiv 1701 07274"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3813-6"},{"key":"ref101","article-title":"Symphony generation with permutation invariant language model","author":"liu","year":"2022","journal-title":"arXiv 2205 05448"},{"key":"ref25","article-title":"Music composition with deep learning: A review","author":"hernandez-olivan","year":"2021","journal-title":"arXiv 2108 12290"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747802"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/MMRP.2019.00022"},{"key":"ref51","article-title":"MeloForm: Generating melody with musical form based on expert systems and neural networks","author":"lu","year":"2022","journal-title":"arXiv 2208 14345"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1080\/09298215.2018.1511736"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2831228"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3868-4"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195373233.001.0001"},{"journal-title":"An Introduction to Multiagent Systems","year":"2009","author":"wooldridge","key":"ref148"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55750-2_8"},{"key":"ref59","article-title":"Symbolic music generation with diffusion models","author":"mittal","year":"2021","journal-title":"arXiv 2103 16091"},{"key":"ref58","article-title":"MIDI-sandwich2: RNN-based hierarchical multi-modal fusion generation VAE networks for multi-track symbolic music generation","author":"liang","year":"2019","journal-title":"arXiv 1909 03522"},{"key":"ref57","article-title":"PIANOTREE VAE: Structured representation learning for polyphonic music","author":"wang","year":"2020","journal-title":"arXiv 2008 07118"},{"key":"ref56","first-page":"1","article-title":"Latent constraints: Learning to generate conditionally from unconditional generative models","author":"engel","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"article-title":"MidiMe: Personalizing a musicvae model with user data","year":"2019","author":"dinculescu","key":"ref55"},{"article-title":"Learning a latent space of multitrack measures","year":"2018","author":"simon","key":"ref54"},{"key":"ref53","first-page":"4364","article-title":"A hierarchical latent vector model for learning long-term structure in music","author":"roberts","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref52","first-page":"7673","article-title":"Latent normalizing flows for discrete sequences","author":"ziegler","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref40","article-title":"Controllable deep melody generation via hierarchical music structure representation","author":"dai","year":"2021","journal-title":"arXiv 2109 00663"},{"key":"ref4","article-title":"Evolutionary methods for musical composition","author":"wiggins","year":"1999","journal-title":"Int J Comput Anticipatory Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1080\/09298218908570541"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10601-010-9101-4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2307\/1575226"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.9126"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1525\/mp.2013.31.1.32"},{"key":"ref7","first-page":"167","article-title":"Sampling variations of sequences for structured music generation","author":"pachet","year":"2017","journal-title":"Proc ISMIR"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICSC.2018.00077"},{"key":"ref46","article-title":"Interactive music generation with positional constraints using anticipation-RNNs","author":"hadjeres","year":"2017","journal-title":"arXiv 1709 06404"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3758-9"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2020.3019642"},{"key":"ref47","article-title":"Learning to generate music with sentiment","author":"ferreira","year":"2021","journal-title":"arXiv 2103 06125"},{"journal-title":"BandNet A neural network-based multi-instrument beatles-style MIDI music composition machine","year":"2018","author":"zhou","key":"ref42"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-11881-1"},{"key":"ref44","article-title":"Learning to generate music with BachProp","author":"colombo","year":"2018","journal-title":"arXiv 1812 06669"},{"key":"ref43","first-page":"454","article-title":"Rhythm, chord and melody generation for lead sheets using recurrent neural networks","author":"boom","year":"2019","journal-title":"Proc Eur Conf Mach Learn Knowl Discovery Databases"},{"article-title":"Evaluation metrics for language models","year":"1998","author":"chen","key":"ref127"},{"key":"ref126","first-page":"1068","article-title":"Neural audio synthesis of musical notes with WaveNet autoencoders","volume":"70","author":"engel","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref125","first-page":"1","article-title":"SING: Symbol-to-instrument neural generator","volume":"31","author":"d\u00e9fossez","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1162\/comj.2009.33.2.48"},{"key":"ref73","article-title":"Learning style-aware symbolic music representations by adversarial autoencoders","author":"valenti","year":"2020","journal-title":"arXiv 2001 05494"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852291"},{"key":"ref71","first-page":"1","article-title":"Improved training of Wasserstein GANs","volume":"30","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"Groovae Generating and Controlling Expressive Drum Performances","year":"2019","author":"gillick","key":"ref129"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11312"},{"key":"ref128","first-page":"3060","article-title":"Graph neural network for music score data and modeling expressive piano performance","author":"jeong","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.98"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2018.00123"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.5920\/JCMS.2017.09"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00114"},{"key":"ref75","first-page":"876","article-title":"CONLON: A pseudo-song generator based on a new pianoroll, Wasserstein autoencoders, and optimal interpolations","author":"angioloni","year":"2020","journal-title":"Proc ISMIR"},{"key":"ref133","article-title":"A note on the evaluation of generative models","author":"theis","year":"2015","journal-title":"arXiv 1511 01844"},{"key":"ref134","article-title":"Chord generation from symbolic melody using BLSTM networks","author":"lim","year":"2017","journal-title":"arXiv 1712 01011"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"journal-title":"Counterpoint by Convolution","year":"2019","author":"huang","key":"ref131"},{"key":"ref79","first-page":"437","article-title":"Neural symbolic music genre transfer insights","author":"brunner","year":"2019","journal-title":"Proc Eur Conf Mach Learn Knowl Discovery Databases"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55750-2_9"},{"article-title":"On the generalized distance in statistics","year":"1936","author":"mahalanobis","key":"ref136"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966292"},{"key":"ref138","article-title":"Explicitly conditioned melody generation: A case study with interdependent RNNs","author":"genchel","year":"2019","journal-title":"arXiv 1907 05208"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413721"},{"key":"ref60","article-title":"Music SketchNet: Controllable music generation via factorized representations of pitch and rhythm","author":"chen","year":"2020","journal-title":"arXiv 2008 01291"},{"key":"ref139","first-page":"1","article-title":"Guided music synthesis with variable Markov Oracle","author":"wang","year":"2014","journal-title":"Proc 10th Artif Intell Interact Digit Entertainment Conf"},{"key":"ref62","article-title":"MIDI-VAE: Modeling dynamics and instrumentation of music with applications to style transfer","author":"brunner","year":"2018","journal-title":"arXiv 1809 07600"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461724"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683106"},{"key":"ref64","article-title":"Music FaderNets: Controllable music generation based on high-level features via low-level feature modelling","author":"tan","year":"2020","journal-title":"arXiv 2007 15474"},{"key":"ref65","first-page":"1","article-title":"Fader networks: Manipulating images by sliding attributes","volume":"30","author":"lample","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-020-10241-8"},{"key":"ref66","article-title":"Learning to traverse latent spaces for musical score inpainting","author":"pati","year":"2019","journal-title":"arXiv 1907 01164"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.5920\/jcms.2018.01"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1145\/344779.344972"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1162\/014892602760137158"},{"key":"ref68","first-page":"1","article-title":"C-RNN-GAN: A continuous recurrent neural network with adversarial training","author":"mogren","year":"2016","journal-title":"Proc Constructive Mach Learn Workshop (CML) NIPS"},{"key":"ref143","first-page":"97","article-title":"A scape plot representation for visualizing repetitive structures of music recordings","author":"m\u00fcller","year":"2012","journal-title":"Proc ISMIR"},{"key":"ref69","article-title":"Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models","author":"lima guimaraes","year":"2017","journal-title":"arXiv 1705 10843"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1162\/COMJ_r_00008"},{"key":"ref144","article-title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training","author":"mogren","year":"2016","journal-title":"arXiv 1611 09904"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2020.00014"},{"key":"ref145","first-page":"1","article-title":"Maximum entropy model for melodic patterns","author":"sakellariou","year":"2015","journal-title":"Proc ICML Workshop Constructive Mach Learn"},{"key":"ref109","first-page":"1","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"radford","year":"2019","journal-title":"OpenAIRE blog"},{"key":"ref95","first-page":"1899","article-title":"Encoding musical style with transformer autoencoders","author":"choi","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref108","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2018","journal-title":"arXiv 1810 04805"},{"key":"ref94","article-title":"Automatic composition of guitar tabs by transformers and groove modeling","author":"chen","year":"2020","journal-title":"arXiv 2008 01431"},{"key":"ref107","article-title":"Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription","author":"boulanger-lewandowski","year":"2012","journal-title":"arXiv 1206 6392"},{"key":"ref93","article-title":"The jazz transformer on the front line: Exploring the shortcomings of AI-composed music through quantitative measures","author":"wu","year":"2020","journal-title":"arXiv 2008 01307"},{"key":"ref106","first-page":"29","article-title":"The neural autoregressive distribution estimator","author":"larochelle","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref92","article-title":"FIGARO: Generating symbolic music with fine-grained artistic control","author":"von r\u00fctte","year":"2022","journal-title":"arXiv 2201 10936"},{"key":"ref91","article-title":"Structure-enhanced pop music generation via harmony-aware learning","author":"zhang","year":"2021","journal-title":"arXiv 2109 06441"},{"key":"ref105","first-page":"1","article-title":"Transformer-NADE for piano performances","author":"hawthorne","year":"2018","journal-title":"Proc NIPS 2nd Workshop Mach Learn Creativity Design"},{"key":"ref90","article-title":"MuseMorphose: Full-song and fine-grained music style transfer with one transformer VAE","author":"wu","year":"2021","journal-title":"arXiv 2105 04090"},{"key":"ref104","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"ester","year":"1996","journal-title":"Proc KDD"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.427"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3161851"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/APSIPAASC47483.2019.9023224"},{"journal-title":"Tuning recurrent neural networks with reinforcement learning","year":"2017","author":"jaques","key":"ref112"},{"key":"ref110","article-title":"LakhNES: Improving multi-instrumental music generation with cross-domain pre-training","author":"donahue","year":"2019","journal-title":"arXiv 1907 04868"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16091"},{"key":"ref99","article-title":"Modeling baroque two-part counterpoint with neural machine translation","author":"nichols","year":"2020","journal-title":"arXiv 2006 14221"},{"key":"ref96","article-title":"MMM: Exploring conditional multi-track music generation with the transformer","author":"ens","year":"2020","journal-title":"arXiv 2008 06048"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475195"},{"key":"ref10","first-page":"1362","article-title":"Deepbach: A steerable model for bach chorales generation","author":"hadjeres","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","first-page":"19","article-title":"BachBot: Automatic composition in the style of bach chorales","volume":"8","author":"liang","year":"2016","journal-title":"Cambridge Univ"},{"key":"ref12","article-title":"Jukebox: A generative model for music","author":"dhariwal","year":"2020","journal-title":"arXiv 2005 00341"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5946971"},{"key":"ref14","article-title":"AI song contest: Human-AI co-creation in songwriting","author":"huang","year":"2020","journal-title":"arXiv 2010 05388"},{"journal-title":"Deep Learning Techniques for Music Generation","year":"2019","author":"briot","key":"ref15"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/WASPAA.2019.8937261"},{"key":"ref16","article-title":"A comprehensive survey on deep music generation: Multi-level representations, algorithms, evaluations, and future directions","author":"ji","year":"2020","journal-title":"arXiv 2011 06801"},{"key":"ref118","article-title":"Style imitation and chord invention in polyphonic music with exponential families","author":"hadjeres","year":"2016","journal-title":"arXiv 1609 05152"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1001705107"},{"key":"ref81","article-title":"CAN: Creative adversarial networks, generating ‘art’ by learning about styles and deviating from style norms","author":"elgammal","year":"2017","journal-title":"arXiv 1706 07068"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref84","article-title":"Music transformer","author":"huang","year":"2018","journal-title":"arXiv 1809 04281"},{"key":"ref18","first-page":"139","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2074"},{"key":"ref119","article-title":"AccoMontage: Accompaniment arrangement via phrase selection and style transfer","author":"zhao","year":"2021","journal-title":"arXiv 2108 11213"},{"key":"ref19","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"arXiv 1312 6114"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5413"},{"key":"ref113","article-title":"Polyphonic music composition with LSTM neural networks and reinforcement learning","author":"kumar","year":"2019","journal-title":"arXiv 1902 01973"},{"key":"ref116","article-title":"Maximum entropy models for generation of expressive music","author":"moulieras","year":"2016","journal-title":"arXiv 1610 03606"},{"key":"ref80","article-title":"Can GAN originate new electronic dance music genres?—Generating novel rhythm patterns using GAN with genre ambiguity loss","author":"tokui","year":"2020","journal-title":"arXiv 2011 13062"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/RCAR52367.2021.9517538"},{"key":"ref89","first-page":"1","article-title":"Improving polyphonic music models with feature-rich encoding","author":"peracha","year":"2020","journal-title":"Proc ISMIR"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-012-9156-1"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3849-7"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.2307\/3399585"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4020-6710-5_3"},{"journal-title":"MuseNet","year":"2019","author":"payne","key":"ref85"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2990746"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054554"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413671"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09966445.pdf?arnumber=9966445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:37:07Z","timestamp":1672083427000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9966445\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":151,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3225689","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2022]]}}}