{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T13:54:50Z","timestamp":1762782890239,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T00:00:00Z","timestamp":1676678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"ANR project TheVoice","doi-asserted-by":"publisher","award":["ANR-17-CE23-0025","ANR-19-CE38-0001-03","2020-AD011011378R1","2021-AD011011177R1"],"award-info":[{"award-number":["ANR-17-CE23-0025","ANR-19-CE38-0001-03","2020-AD011011378R1","2021-AD011011177R1"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"ANR projet ARS","doi-asserted-by":"publisher","award":["ANR-17-CE23-0025","ANR-19-CE38-0001-03","2020-AD011011378R1","2021-AD011011177R1"],"award-info":[{"award-number":["ANR-17-CE23-0025","ANR-19-CE38-0001-03","2020-AD011011378R1","2021-AD011011177R1"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"name":"GENCI","award":["ANR-17-CE23-0025","ANR-19-CE38-0001-03","2020-AD011011378R1","2021-AD011011177R1"],"award-info":[{"award-number":["ANR-17-CE23-0025","ANR-19-CE38-0001-03","2020-AD011011378R1","2021-AD011011177R1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Voice conversion (VC) consists of digitally altering the voice of an individual to manipulate part of its content, primarily its identity, while maintaining the rest unchanged. Research in neural VC has accomplished considerable breakthroughs with the capacity to falsify a voice identity using a small amount of data with a highly realistic rendering. This paper goes beyond voice identity manipulation and presents an original neural architecture that allows the manipulation of voice attributes (e.g., gender and age). The proposed architecture is inspired by the fader network, transferring the same ideas to voice manipulation. The information conveyed by the speech signal is disentangled into interpretative voice attributes by means of minimizing adversarial loss to make the encoded information mutually independent while preserving the capacity to generate a speech signal from the disentangled codes. During inference for voice conversion, the disentangled voice attributes can be manipulated and the speech signal can be generated accordingly. For experimental evaluation, the proposed method is applied to the task of voice gender conversion using the freely available VCTK dataset. Quantitative measurements of mutual information between the variables of speaker identity and speaker gender show that the proposed architecture can learn gender-independent representation of speakers. Additional measurements of speaker recognition indicate that speaker identity can be recognized accurately from the gender-independent representation. Finally, a subjective experiment conducted on the task of voice gender manipulation shows that the proposed architecture can convert voice gender with very high efficiency and good naturalness.<\/jats:p>","DOI":"10.3390\/e25020375","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T03:56:07Z","timestamp":1676865367000},"page":"375","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2756-5100","authenticated-orcid":false,"given":"Laurent","family":"Benaroya","sequence":"first","affiliation":[{"name":"Analysis\/Synthesis Team\u2014STMS, IRCAM, Sorbonne University, CNRS, French Ministry of Culture, 75004 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5236-5306","authenticated-orcid":false,"given":"Nicolas","family":"Obin","sequence":"additional","affiliation":[{"name":"Analysis\/Synthesis Team\u2014STMS, IRCAM, Sorbonne University, CNRS, French Ministry of Culture, 75004 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6136-4391","authenticated-orcid":false,"given":"Axel","family":"Roebel","sequence":"additional","affiliation":[{"name":"Analysis\/Synthesis Team\u2014STMS, IRCAM, Sorbonne University, CNRS, French Ministry of Culture, 75004 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0167-6393(94)00053-D","article-title":"Acoustic Characteristics of Speaker Individuality: Control and Conversion","volume":"16","author":"Kuwabara","year":"1995","journal-title":"Speech Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/89.661472","article-title":"Continuous Probabilistic Transform for Voice Conversion","volume":"6","author":"Stylianou","year":"1998","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Toda, T., Chen, L.H., Saito, D., Villavicencio, F., Wester, M., Wu, Z., and Yamagishi, J. (2016, January 8\u201312). The Voice Conversion Challenge 2016. Proceedings of the ISCA Interspeech, San Francisco, CA, USA.","DOI":"10.21437\/Interspeech.2016-1066"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lorenzo-Trueba, J., Yamagishi, J., Toda, T., Saito, D., Villavicencio, F., Kinnunen, T., and Ling, Z. (2018, January 26\u201329). The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods. Proceedings of the Speaker Odyssey: The Speaker and Language Recognition Workshop, Les Sables d\u2019Olonne, France.","DOI":"10.21437\/Odyssey.2018-28"},{"key":"ref_5","unstructured":"Zhao, Y., Huang, W.C., Tian, X., Yamagishi, J., Das, R.K., Kinnunen, T., Ling, Z., and Toda, T. (2020, January 25\u201329). Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion. Proceedings of the ISCA Interspeech, Shanghai, China."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lorenzo-Trueba, J., Fang, F., Wang, X., Echizen, I., Yamagishi, J., and Kinnunen, T. (2018, January 26\u201329). Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama\u2019s voice using GAN, WaveNet and low-quality found data. Proceedings of the Speaker Odyssey: The Speaker and Language Recognition Workshop, Les Sables d\u2019Olonne, France.","DOI":"10.21437\/Odyssey.2018-34"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lal Srivastava, B.M., Vauquier, N., Sahidullah, M., Bellet, A., Tommasi, M., and Vincent, E. (2020, January 4\u20138). Evaluating Voice Conversion-Based Privacy Protection against Informed Attackers. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053868"},{"key":"ref_8","unstructured":"Ericsson, D., \u00d6stberg, A., Listo Zec, E., Martinsson, J., and Mogren, O. (2020, January 12\u201318). Adversarial representation learning for private speech generation. Proceedings of the Workshop on Self-supervision in Audio and Speech at the International Conference on Machine Learning (ICML), Virtual."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, D., Yu, J., Wu, X., Liu, S., Sun, L., Liu, X., and Meng, H. (2020, January 4\u20138). End-To-End Voice Conversion Via Cross-Modal Knowledge Distillation for Dysarthric Speech Reconstruction. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054596"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Toda, T., Ohtani, Y., and Shikano, K. (2007, January 15\u201320). One-to-Many and Many-to-One Voice Conversion Based on Eigenvoices. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Honolulu, HI, USA.","DOI":"10.1109\/ICASSP.2007.367303"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Desai, S., Raghavendra, E.V., Yegnanarayana, B., Black, A.W., and Prahallad, K. (2009, January 19\u201324). Voice conversion using Artificial Neural Networks. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4960478"},{"key":"ref_12","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Networks. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_13","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014, January 8\u201313). Sequence to Sequence Learning with Neural Networks. Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_14","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7\u20139). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hsu, C.C., Hwang, H.T., Wu, Y.C., Tsao, Y., and Wang, H.M. (2016, January 13\u201316). Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Republic of Korea.","DOI":"10.1109\/APSIPA.2016.7820786"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kaneko, T., and Kameoka, H. (2017). Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks. arXiv.","DOI":"10.23919\/EUSIPCO.2018.8553236"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kaneko, T., Kameoka, H., Tanaka, K., and Hojo, N. (2019, January 12\u201317). CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682897"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fang, F., Yamagishi, J., Echizen, I., and Lorenzo-Trueba, J. (2018, January 15\u201320). High-Quality Nonparallel Voice Conversion Based on Cycle-Consistent Adversarial Network. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462342"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tanaka, K., Kameoka, H., Kaneko, T., and Hojo, N. (2019, January 12\u201317). AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683282"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/TASLP.2020.3001456","article-title":"ConvS2S-VC: Fully Convolutional Sequence-to-Sequence Voice Conversion","volume":"28","author":"Kameoka","year":"2020","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kameoka, H., Kaneko, T., Tanaka, K., and Hojo, N. (2018, January 18\u201321). StarGAN-VC: Non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks. Proceedings of the 2018 IEEE Spoken Language Technology Workshop (SLT), Athens, Greece.","DOI":"10.1109\/SLT.2018.8639535"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kaneko, T., Kameoka, H., Tanaka, K., and Hojo, N. (2019, January 15\u201319). StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion. Proceedings of the ISCA Interspeech, Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2236"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, C., Horgan, M., Kumar, V., Vasco, C., and Darcy, D. (2018, January 2\u20136). Voice Conversion with Conditional SampleRNN. Proceedings of the ISCA Interspeech, Hyderabad, India.","DOI":"10.21437\/Interspeech.2018-1121"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lu, H., Wu, Z., Dai, D., Li, R., Kang, S., Jia, J., and Meng, H. (2019, January 15\u201319). One-Shot Voice Conversion with Global Speaker Embeddings. Proceedings of the ISCA Interspeech, Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2365"},{"key":"ref_26","unstructured":"Qian, K., Zhang, Y., Chang, S., Yang, X., and Hasegawa-Johnson, M. (2019, January 10\u201315). AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss. Proceedings of the International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Weiss, R.J., Zen, H., Wu, Y., Chen, Z., Skerry-Ryan, R.J., Jia, Y., Rosenberg, A., and Ramabhadran, B. (2019, January 15\u201319). Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning. Proceedings of the ISCA Interspeech, Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2668"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, L., Li, K., Wang, H., Kang, S., and Meng, H. (2016, January 11\u201315). Phonetic Posteriorgrams for Many-to-One Voice Conversion without Parallel Data Training. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Seattle, WA, USA.","DOI":"10.1109\/ICME.2016.7552917"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mohammadi, S.H., and Kim, T. (2019, January 15\u201319). One-Shot Voice Conversion with Disentangled Representations by Leveraging Phonetic Posteriorgrams. Proceedings of the ISCA Interspeech, Graz, Austria.","DOI":"10.21437\/Interspeech.2019-1798"},{"key":"ref_30","unstructured":"Jia, Y., Zhang, Y., Weiss, R.J., Wang, Q., Shen, J., Ren, F., Chen, Z., Nguyen, P., Pang, R., and Lopez-Moreno, I. (2018, January 3\u20138). Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_31","unstructured":"Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A. (2018). Towards a Definition of Disentangled Representations. arXiv."},{"key":"ref_32","unstructured":"Tishby, N., and Zaslavsky, N. (May, January 26). Deep Learning and the Information Bottleneck Principle. Proceedings of the IEEE Information Theory Workshop (ITW), Jerusalem, Israel."},{"key":"ref_33","unstructured":"Lample, G., Zeghidour, N., Usunier, N., Bordes, A., Denoyer, L., and Ranzato, M. (2017, January 4\u20139). Fader Networks: Manipulating Images by Sliding Attributes. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_34","unstructured":"Belghazi, I., Rajeswar, S., Baratin, A., Hjelm, R.D., and Courville, A.C. (2018, January 10\u201315). MINE: Mutual Information Neural Estimation. Proceedings of the International Conference on Machine Learning (PMLR), Stockholm, Sweden."},{"key":"ref_35","unstructured":"Qian, K., Zhang, Y., Chang, S., Hasegawa-Johnson, M., and Cox, D. (2020, January 12\u201318). Unsupervised Speech Decomposition via Triple Information Bottleneck. Proceedings of the International Conference on Machine Learning (ICML), Virtual."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TASLP.2019.2960721","article-title":"Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process. (TASLP)"},{"key":"ref_37","unstructured":"Yuan, S., Cheng, P., Zhang, R., Hao, W., Gan, Z., and Carin, L. (2021, January 3\u20137). Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning. Proceedings of the International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J., Kim, S., and Choo, J. (2018, January 18\u201323). StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref_39","first-page":"220","article-title":"ClsGAN: Selective Attribute Editing Based On Classification Adversarial Network","volume":"133","author":"Ying","year":"2019","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bous, F., Benaroya, L., Obin, N., and Roebel, A. (2021, January 23\u201327). Voice Reenactment with F0 and timing constraints and adversarial learning of conversions. Proceedings of the European conference on signal processing (EUSIPCO), Dublin, Ireland.","DOI":"10.23919\/EUSIPCO55093.2022.9909854"},{"key":"ref_41","unstructured":"Qin, Z., Kim, D., and Gedeon, T. (2015, January 6\u201311). Rethinking softmax with cross-entropy: Neural network classifier as mutual information estimator. Proceedings of the International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, L.J., Ling, Z.H., Jiang, Y., Zhou, M., and Dai, L.R. (2018, January 2\u20136). WaveNet Vocoder with Limited Training Data for Voice Conversion. Proceedings of the Interspeech 2018, Hyderabad, India.","DOI":"10.21437\/Interspeech.2018-1190"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TASSP.1984.1164317","article-title":"Signal Estimation from Modified Short-Time Fourier Transform","volume":"32","author":"Griffin","year":"1984","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Prenger, R., Valle, R., and Catanzaro, B. (2019, January 12\u201317). WaveGlow: A Flow-based Generative Network for Speech Synthesis. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683143"},{"key":"ref_45","unstructured":"Yamagishi, J., Veaux, C., and Macdonald, K. (2019). CSTR VCTK Corpus: English Multi-Speaker Corpus for CSTR Voice Cloning Toolkit (Version 0.92), The Centre for Speech Technology Research (CSTR), University of Edinburgh."},{"key":"ref_46","unstructured":"Farner, S., Roebel, A., and Rodet, X. (2009, January 11\u201313). Natural transformation of type and nature of the voice for extending vocal repertoire in high-fidelity applications. Proceedings of the Audio Engineering Society Conference: 35th International Conference: Audio for Games, London, UK."},{"key":"ref_47","unstructured":"Gao, W., Kannan, S., Oh, S., and Viswanath, P. (2017, January 4\u20139). Estimating Mutual Information for Discrete-Continuous Mixtures. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1121\/1.1906875","article-title":"Control methods used in a study of the vowels","volume":"24","author":"Peterson","year":"1952","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1121\/1.2697522","article-title":"Age, sex, and vowel dependencies of acoustic measures related to the voice source","volume":"121","author":"Iseli","year":"2007","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"R\u00f6bel, A. (2010, January 26\u201330). Shape-invariant speech transformation with the phase vocoder. Proceedings of the Proc. International Conference on Spoken Language Processing (InterSpeech), Chiba, Japan.","DOI":"10.21437\/Interspeech.2010-592"},{"key":"ref_51","unstructured":"R\u00f6bel, A., and Rodet, X. (2005, January 20\u201322). Efficient Spectral Envelope Estimation and its application to pitch shifting and envelope preservation. Proceedings of the 8th International Conference on Digital Audio Effects (DAFx\u201905), Madrid, Spain."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/375\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:35:52Z","timestamp":1760121352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/2\/375"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,18]]},"references-count":51,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["e25020375"],"URL":"https:\/\/doi.org\/10.3390\/e25020375","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2023,2,18]]}}}