{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:36:28Z","timestamp":1777476988714,"version":"3.51.4"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887109","type":"print"},{"value":"9783031887116","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-88711-6_14","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T17:14:49Z","timestamp":1743786889000},"page":"218-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Uncertainty Estimation in\u00a0the\u00a0Real World: A Study on\u00a0Music Emotion Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3878-5048","authenticated-orcid":false,"given":"Karn N.","family":"Watcharasupat","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8156-3715","authenticated-orcid":false,"given":"Yiwei","family":"Ding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9254-4106","authenticated-orcid":false,"given":"T. Aleksandra","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7838-9614","authenticated-orcid":false,"given":"Pavan","family":"Seshadri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6319-578X","authenticated-orcid":false,"given":"Alexander","family":"Lerch","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","volume":"76","author":"M Abdar","year":"2021","unstructured":"Abdar, M., et al.: A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243\u2013297 (2021). https:\/\/doi.org\/10.1016\/j.inffus.2021.05.008","journal-title":"Inf. Fusion"},{"issue":"3","key":"14_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0173392","volume":"12","author":"A Aljanaki","year":"2017","unstructured":"Aljanaki, A., Yang, Y.H., Soleymani, M.: Developing a benchmark for emotional analysis of music. PLoS ONE 12(3), e0173392 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0173392","journal-title":"PLoS ONE"},{"issue":"7","key":"14_CR3","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1109\/TASLP.2017.2693565","volume":"25","author":"YA Chen","year":"2017","unstructured":"Chen, Y.A., Wang, J.C., Yang, Y.H., Chen, H.H.: Component tying for mixture model adaptation in personalization of music emotion recognition. IEEE\/ACM Trans. Audio Speech Lang. Process. 25(7), 1409\u20131420 (2017). https:\/\/doi.org\/10.1109\/TASLP.2017.2693565","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Cheuk, K.W., Luo, Y.J., Balamurali, B.T., Roig, G., Herremans, D.: Regression-based music emotion prediction using triplet neural networks. In: Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20137. IEEE, Glasgow, United Kingdom (2020). https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207212","DOI":"10.1109\/IJCNN48605.2020.9207212"},{"issue":"4","key":"14_CR5","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/TAFFC.2016.2628794","volume":"9","author":"YH Chin","year":"2018","unstructured":"Chin, Y.H., Wang, J.C., Wang, J.C., Yang, Y.H.: Predicting the probability density function of music emotion using emotion space mapping. IEEE Trans. Affect. Comput. 9(4), 541\u2013549 (2018). https:\/\/doi.org\/10.1109\/TAFFC.2016.2628794","journal-title":"IEEE Trans. Affect. Comput."},{"key":"14_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2022.997282","volume":"16","author":"X Cui","year":"2022","unstructured":"Cui, X., Wu, Y., Wu, J., You, Z., Xiahou, J., Ouyang, M.: A review: music-emotion recognition and analysis based on EEG signals. Front. Neuroinform. 16, 997282 (2022). https:\/\/doi.org\/10.3389\/fninf.2022.997282","journal-title":"Front. Neuroinform."},{"key":"14_CR7","unstructured":"Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with bernoulli approximate variational inference. In: Workshop Track Proceedings of the 4th International Conference on Learning Representations (2016)"},{"issue":"S1","key":"14_CR8","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski, J., et al.: A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56(S1), 1513\u20131589 (2023). https:\/\/doi.org\/10.1007\/s10462-023-10562-9","journal-title":"Artif. Intell. Rev."},{"key":"14_CR9","doi-asserted-by":"publisher","unstructured":"Imbrasaite, V., Baltrusaitis, T., Robinson, P.: Emotion tracking in music using continuous conditional random fields and relative feature representation. In: Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp.\u00a01\u20136. IEEE, San Jose, CA, USA (2013). https:\/\/doi.org\/10.1109\/ICMEW.2013.6618357","DOI":"10.1109\/ICMEW.2013.6618357"},{"key":"14_CR10","doi-asserted-by":"publisher","unstructured":"Imbrasaite, V., Baltrusaitis, T., Robinson, P.: CCNF for continuous emotion tracking in music: comparison with CCRF and relative feature representation. In: Proceedings of the 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp.\u00a01\u20136. IEEE, Chengdu, China (2014). https:\/\/doi.org\/10.1109\/ICMEW.2014.6890697","DOI":"10.1109\/ICMEW.2014.6890697"},{"key":"14_CR11","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"14_CR12","unstructured":"Kirchhof, M., Mucs\u00e1nyi, B., Oh, S.J., Kasneci, E.: URL: a representation learning benchmark for transferable uncertainty estimates. In: Advances in Neural Information Processing Systems, vol.\u00a036, pp. 13956\u201313980. Curran Associates, Inc. (2023)"},{"key":"14_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2019.106816","volume":"142","author":"Y Kwon","year":"2020","unstructured":"Kwon, Y., Won, J.H., Kim, B.J., Paik, M.C.: Uncertainty quantification using Bayesian neural networks in classification: application to biomedical image segmentation. Comput. Stat. Data Anal. 142, 106816 (2020). https:\/\/doi.org\/10.1016\/j.csda.2019.106816","journal-title":"Comput. Stat. Data Anal."},{"key":"14_CR14","doi-asserted-by":"publisher","unstructured":"Lerch, A.: Mood recognition. In: An Introduction to Audio Content Analysis: Music Information Retrieval Tasks and Applications, pp. 127\u2013216. IEEE (2023). https:\/\/doi.org\/10.1002\/9781119890980.ch7","DOI":"10.1002\/9781119890980.ch7"},{"key":"14_CR15","doi-asserted-by":"publisher","unstructured":"Lionello, M., Aletta, F., Mitchell, A., Kang, J.: Introducing a method for intervals correction on multiple likert scales: a case study on an urban soundscape data collection instrument. Front. Psychol. 11 (2021). https:\/\/doi.org\/10.3389\/fpsyg.2020.602831","DOI":"10.3389\/fpsyg.2020.602831"},{"key":"14_CR16","unstructured":"Mucs\u00e1nyi, B., Kirchhof, M., Oh, S.J.: Benchmarking uncertainty disentanglement: specialized uncertainties for specialized tasks. In: 38th Annual Conference on Neural Information Processing Systems. Vancouver, Canada (2024)"},{"key":"14_CR17","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.: When does label smoothing help? In: Advances in Neural Information Processing Systems, vol.\u00a032, pp. 4694\u20134703. Curran Associates Inc., Red Hook, NY, USA (2019)"},{"key":"14_CR18","doi-asserted-by":"publisher","unstructured":"Ooi, K., Watcharasupat, K.N., Lam, B., Ong, Z.T., Gan, W.S.: Probably pleasant? A neural-probabilistic approach to automatic masker selection for urban soundscape augmentation. In: Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, Singapore, Singapore (2022). https:\/\/doi.org\/10.1109\/icassp43922.2022.9746897","DOI":"10.1109\/icassp43922.2022.9746897"},{"key":"14_CR19","doi-asserted-by":"publisher","unstructured":"Ooi, K., Watcharasupat, K.N., Lam, B., Ong, Z.T., Gan, W.S.: Autonomous soundscape augmentation with multimodal fusion of visual and participant-linked inputs. In: Proceedings of the 2023 International Conference on Acoustics, Speech, and Signal Processing. IEEE, Rhodes Island, Greece (2023). https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10094866","DOI":"10.1109\/ICASSP49357.2023.10094866"},{"issue":"6","key":"14_CR20","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1037\/h0077714","volume":"39","author":"JA Russell","year":"1980","unstructured":"Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980). https:\/\/doi.org\/10.1037\/h0077714","journal-title":"J. Pers. Soc. Psychol."},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Schmidt, E.M., Kim, Y.E.: Prediction of time-varying musical mood distributions from audio. In: Proceedings of the 11th International Society for Music Information Retrieval Conference\u2019. ISMIR, Utrecht, Netherlands (2010)","DOI":"10.1109\/ICMLA.2010.101"},{"key":"14_CR22","doi-asserted-by":"publisher","unstructured":"Schmidt, E.M., Kim, Y.E.: Prediction of time-varying musical mood distributions using kalman filtering. In: Proceedings of the 9th International Conference on Machine Learning and Applications, pp. 655\u2013660. IEEE, Washington, DC, USA (2010). https:\/\/doi.org\/10.1109\/ICMLA.2010.101","DOI":"10.1109\/ICMLA.2010.101"},{"key":"14_CR23","unstructured":"Schmidt, E.M., Kim, Y.E.: Modeling musical emotion dynamics with conditional random fields. In: Proceedings of the 12th International Society for Music Information Retrieval Conference. ISMIR, Miami, FL, USA (2011)"},{"key":"14_CR24","unstructured":"Seitzer, M., Tavakoli, A., Antic, D., Martius, G.: On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks. In: Proceedings of the 10th International Conference on Learning Representations (2022)"},{"key":"14_CR25","unstructured":"Smyth, P., Fayyad, U.M., Burl, M.C., Perona, P., Baldi, P.: Inferring ground truth from subjective labelling of venus images. In: Advances in Neural Information Processing Systems, vol.\u00a07, pp. 1085\u20131092 (1994)"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Tan, S.L., Pfordresher, P., Harr\u00e9, R.: Psychology of Music: From Sound to Significance, 2nd edn. Routledge, Abingdon (2018)","DOI":"10.4324\/9781315648026"},{"key":"14_CR27","doi-asserted-by":"publisher","unstructured":"Valdenegro-Toro, M., Mori, D.S.: A deeper look into aleatoric and epistemic uncertainty disentanglement. In: Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1508\u20131516. IEEE Computer Society (2022). https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00157","DOI":"10.1109\/CVPRW56347.2022.00157"},{"key":"14_CR28","doi-asserted-by":"publisher","unstructured":"Wang, J.C., Wang, H.M., Lanckriet, G.: A histogram density modeling approach to music emotion recognition. In: Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 698\u2013702. IEEE, South Brisbane, Queensland, Australia (2015). https:\/\/doi.org\/10.1109\/ICASSP.2015.7178059","DOI":"10.1109\/ICASSP.2015.7178059"},{"key":"14_CR29","doi-asserted-by":"publisher","unstructured":"Wang, J.C., Yang, Y.H., Wang, H.M., Jeng, S.K.: The acoustic emotion gaussians model for emotion-based music annotation and retrieval. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 89\u201398. ACM, Nara Japan (2012). https:\/\/doi.org\/10.1145\/2393347.2393367","DOI":"10.1145\/2393347.2393367"},{"issue":"1","key":"14_CR30","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/TAFFC.2015.2397457","volume":"6","author":"JC Wang","year":"2015","unstructured":"Wang, J.C., Yang, Y.H., Wang, H.M., Jeng, S.K.: Modeling the affective content of music with a Gaussian mixture model. IEEE Trans. Affect. Comput. 6(1), 56\u201368 (2015). https:\/\/doi.org\/10.1109\/TAFFC.2015.2397457","journal-title":"IEEE Trans. Affect. Comput."},{"key":"14_CR31","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1109\/lsp.2022.3194419","volume":"29","author":"KN Watcharasupat","year":"2022","unstructured":"Watcharasupat, K.N., Ooi, K., Lam, B., Wong, T., Ong, Z.T., Gan, W.S.: Autonomous in-situ soundscape augmentation via joint selection of masker and gain. IEEE Sig. Process. Lett. 29, 1749\u20131753 (2022). https:\/\/doi.org\/10.1109\/lsp.2022.3194419","journal-title":"IEEE Sig. Process. Lett."},{"key":"14_CR32","doi-asserted-by":"publisher","unstructured":"Won, M., Hung, Y.N., Le, D.: A foundation model for music informatics. In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1226\u20131230. IEEE, Republic of Seoul, Korea (2024). https:\/\/doi.org\/10.1109\/ICASSP48485.2024.10448314","DOI":"10.1109\/ICASSP48485.2024.10448314"},{"key":"14_CR33","doi-asserted-by":"publisher","unstructured":"Xiao, Y., Wang, W.Y.: Quantifying uncertainties in natural language processing tasks. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI\u201919\/IAAI\u201919\/EAAI\u201919, vol.\u00a033, pp. 7322\u20137329. AAAI Press, Honolulu, Hawaii, USA (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33017322","DOI":"10.1609\/aaai.v33i01.33017322"},{"key":"14_CR34","doi-asserted-by":"publisher","DOI":"10.1201\/b10731","volume-title":"Music Emotion Recognition","author":"YH Yang","year":"2011","unstructured":"Yang, Y.H., Chen, H.H.: Music Emotion Recognition. CRC Press, Boca Raton (2011). https:\/\/doi.org\/10.1201\/b10731"},{"issue":"7","key":"14_CR35","doi-asserted-by":"publisher","first-page":"2184","DOI":"10.1109\/TASL.2011.2118752","volume":"19","author":"YH Yang","year":"2011","unstructured":"Yang, Y.H., Chen, H.H.: Prediction of the distribution of perceived music emotions using discrete samples. IEEE Trans. Audio Speech Lang. Process. 19(7), 2184\u20132196 (2011). https:\/\/doi.org\/10.1109\/TASL.2011.2118752","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"14_CR36","doi-asserted-by":"publisher","unstructured":"Yang, Y.H., Su, Y.F., Lin, Y.C., Chen, H.H.: Music emotion recognition: the role of individuality. In: Proceedings of the International Workshop on Human-centered Multimedia, pp. 13\u201322. ACM, Augsburg Bavaria Germany (2007). https:\/\/doi.org\/10.1145\/1290128.1290132","DOI":"10.1145\/1290128.1290132"},{"key":"14_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108418","volume":"243","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Chan, F., Mahadevan, S.: Explainable machine learning in image classification models: an uncertainty quantification perspective. Knowl.-Based Syst. 243, 108418 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.108418","journal-title":"Knowl.-Based Syst."}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88711-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T17:14:53Z","timestamp":1743786893000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88711-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887109","9783031887116"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88711-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}