{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:09:36Z","timestamp":1777734576511,"version":"3.51.4"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032046239","type":"print"},{"value":"9783032046246","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04624-6_2","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:34:36Z","timestamp":1758000876000},"page":"22-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From Notes to\u00a0Keys: A VR Learning Environment for\u00a0Sheet Music Interpretation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7272-6124","authenticated-orcid":false,"given":"Sandeep","family":"Khanna","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0382-8906","authenticated-orcid":false,"given":"Atanu","family":"Saha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6050-3158","authenticated-orcid":false,"given":"Rahul Kumar","family":"Ray","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2501-9969","authenticated-orcid":false,"given":"Rakesh","family":"Patibanda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3431-0483","authenticated-orcid":false,"given":"Chiranjoy","family":"Chattopadhyay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Arthur, P., Khuu, S., Blom, D.: Music sight-reading expertise, visually disrupted score and eye movements. J. Eye Movement Res. 9 (2016). https:\/\/doi.org\/10.16910\/jemr.9.7.1","DOI":"10.16910\/jemr.9.7.1"},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/978-3-030-02284-6_7","volume-title":"Graphics Recognition. Current Trends and Evolutions","author":"A Bar\u00f3","year":"2018","unstructured":"Bar\u00f3, A., Riba, P., Calvo-Zaragoza, J., Forn\u00e9s, A.: Optical music recognition by long short-term memory networks. In: Forn\u00e9s, A., Lamiroy, B. (eds.) GREC 2017. LNCS, vol. 11009, pp. 81\u201395. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-02284-6_7"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Calvo-Zaragoza, J., Gallego, A.J., Pertusa, A.: Recognition of handwritten music symbols with convolutional neural codes, pp. 691\u2013696 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.118","DOI":"10.1109\/ICDAR.2017.118"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Calvo-Zaragoza, J., Jr., J.H., Pacha, A.: Understanding optical music recognition. ACM Comput. Surv. (CSUR) 53(4), 1\u201335 (2020)","DOI":"10.1145\/3397499"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Calvo-Zaragoza, J., Rizo, D.: End-to-end neural optical music recognition of monophonic scores. Appl. Sci. 8(4) (2018). https:\/\/doi.org\/10.3390\/app8040606, https:\/\/www.mdpi.com\/2076-3417\/8\/4\/606","DOI":"10.3390\/app8040606"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Castellanos, F.J., Gallego, A.J., Fujinaga, I.: Deep learning for optical music recognition: a review. TechRxiv (2025). https:\/\/doi.org\/10.36227\/techrxiv.174077177.78767136\/v1","DOI":"10.36227\/techrxiv.174077177.78767136\/v1"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Chiu, S.C., Chen, M.S.: A study on difficulty level recognition of piano sheet music. In: 2012 IEEE International Symposium on Multimedia, pp. 17\u201323 (2012). https:\/\/api.semanticscholar.org\/CorpusID:36545038","DOI":"10.1109\/ISM.2012.11"},{"key":"2_CR8","unstructured":"van Der\u00a0Wel, E., Ullrich, K.: Optical music recognition with convolutional sequence-to-sequence models. arXiv preprint arXiv:1707.04877 (2017)"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Dos\u00a0Santos, J., Zanlorensi, L.A., Oliveira, L.A.: DeepoMR: a deep learning approach for optical music recognition. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 291\u2013298 (2018). https:\/\/doi.org\/10.1109\/SIBGRAPI.2018.00044","DOI":"10.1109\/SIBGRAPI.2018.00044"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Fourie, E.: The processing of music notation: some implications for piano sight-reading. J. Musical Arts Africa 1 (2004). https:\/\/doi.org\/10.2989\/18121000409486685","DOI":"10.2989\/18121000409486685"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Fujinaga, I., Hankinson, A., Pugin, L.: Automatic score extraction with optical music recognition (OMR). Springer Handbook of Systematic Musicology, pp. 299\u2013311 (2018)","DOI":"10.1007\/978-3-662-55004-5_16"},{"key":"2_CR12","unstructured":"Hajic\u00a0Jr, J., Dorfer, M., Widmer, G., Pecina, P.: Towards full-pipeline handwritten OMR with musical symbol detection by u-nets. In: ISMIR, pp. 225\u2013232 (2018)"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Ju, Y., Wang, X., Chen, X.: Research on OMR recognition based on convolutional neural network tensorflow platform. In: 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 688\u2013691. IEEE (2019)","DOI":"10.1109\/ICMTMA.2019.00157"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Lal, S.A., Chattopadhyay, C., Ray, R.K.: An approach for effective CPR trainings in virtual reality with multimodal feedback. In: IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 326\u2013331 (2025)","DOI":"10.1109\/VRW66409.2025.00078"},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Lehmann, A., Ericsson, K.: Sight-reading ability of expert pianists in the context of piano accompanying. Psychomusicology J. Res. Music Cogn. 12, 182\u2013195 (1993). https:\/\/doi.org\/10.1037\/h0094108","DOI":"10.1037\/h0094108"},{"key":"2_CR16","unstructured":"Martinez-Sevilla, J.C., Rosello, A., Rizo, D., Calvo-Zaragoza, J.: On the performance of optical music recognition in the absence of specific training data. In: ISMIR, pp. 319\u2013326 (2023)"},{"key":"2_CR17","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-031-70552-6_4","volume-title":"ICDAR 2024","author":"J Mayer","year":"2024","unstructured":"Mayer, J., Straka, M., Haji\u010d, J., Pecina, P.: Practical end-to-end optical music recognition for pianoform music. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds.) ICDAR 2024. LNCS, vol. 14809, pp. 55\u201373. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70552-6_4"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Ng, W., Nguyen, X.T.: Improving deep-learning-based optical music recognition for camera-based inputs. In: 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 118\u2013121 (2022). https:\/\/doi.org\/10.1109\/AICAS54282.2022.9869934","DOI":"10.1109\/AICAS54282.2022.9869934"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3991\/ijet.v18i13.39021","volume":"18","author":"S Oueida","year":"2023","unstructured":"Oueida, S., Awad, P., Mattar, C.: Augmented reality awareness and latest applications in education: a review. Int. J. Emerging Technol. Learn. (iJET) 18, 21\u201344 (2023). https:\/\/doi.org\/10.3991\/ijet.v18i13.39021","journal-title":"Int. J. Emerging Technol. Learn. (iJET)"},{"key":"2_CR20","unstructured":"Pacha, A., Calvo-Zaragoza, J.: Optical music recognition in mensural notation with region-based convolutional neural networks. In: ISMIR, pp. 240\u2013247 (2018)"},{"key":"2_CR21","unstructured":"Pacha, A., Calvo-Zaragoza, J., Hajic\u00a0Jr, J.: Learning notation graph construction for full-pipeline optical music recognition. In: ISMIR, pp. 75\u201382 (2019)"},{"issue":"3","key":"2_CR22","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1109\/TOH.2019.2933822","volume":"12","author":"P Patel","year":"2019","unstructured":"Patel, P., Ray, R.K., Manivannan, M.: Power law based \u201cout of body\u2019\u2019 tactile funneling for mobile haptics. IEEE Trans. Haptics 12(3), 307\u2013318 (2019)","journal-title":"IEEE Trans. Haptics"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Pietrzak, T., Ray, R.K.: Comparing apparent haptic motion and funneling for the perception of tactile animation illusions on a circular tactile display. IEEE Trans. Haptics (2025)","DOI":"10.1109\/TOH.2025.3552992"},{"key":"2_CR24","unstructured":"Pugin, L.: Optical music recognitoin of early typographic prints using hidden Markov models, pp. 53\u201356 (01 2006)"},{"key":"2_CR25","doi-asserted-by":"publisher","first-page":"1406923","DOI":"10.3389\/frvir.2024.1406923","volume":"5","author":"RK Ray","year":"2024","unstructured":"Ray, R.K., Kumar Vasudevan, M., Manivannan, M.: Electrotactile displays: taxonomy, cross-modality, psychophysics and challenges. Front. Virt. Real. 5, 1406923 (2024)","journal-title":"Front. Virt. Real."},{"key":"2_CR26","doi-asserted-by":"publisher","unstructured":"Rebelo, A., Fujinaga, I., Paszkiewicz, F., Mar\u00e7al, A., Guedes, C., Cardoso, J.: Optical music recognition: state-of-the-art and open issues. International Journal of Multimedia Information Retrieval 1 (2012). https:\/\/doi.org\/10.1007\/s13735-012-0004-6","DOI":"10.1007\/s13735-012-0004-6"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Shatri, E., Fazekas, G.: Knowledge discovery in optical music recognition: Enhancing information retrieval with instance segmentation. arXiv preprint arXiv:2408.15002 (2024)","DOI":"10.5220\/0012947500003838"},{"key":"2_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/0305735608090845","volume":"37","author":"SL Tan","year":"2008","unstructured":"Tan, S.L., Wakefield, E., Jeffries, P.: Musically untrained college students- interpretations of musical notation: sound, silence, loudness, duration, and temporal order. Psychol. Music - PSYCHOL MUSIC 37, 5\u201324 (2008). https:\/\/doi.org\/10.1177\/0305735608090845","journal-title":"Psychol. Music - PSYCHOL MUSIC"},{"key":"2_CR29","unstructured":"Tuggener, L., Elezi, I., Schmidhuber, J., Stadelmann, T.: Deep watershed detector for music object recognition. arXiv preprint arXiv:1805.10548 (2018)"},{"key":"2_CR30","unstructured":"Tuggener, L., Satyawan, Y.P., Pacha, A., Schmidhuber, J., Stadelmann, T.: Deepscoresv2. Technical report, Zenodo (2020)"},{"key":"2_CR31","doi-asserted-by":"publisher","unstructured":"Vo, M., Shin, A.: Handwritten music symbol recognition using convolutional neural networks. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 419\u2013424 (2016). https:\/\/doi.org\/10.1109\/ICFHR.2016.0091","DOI":"10.1109\/ICFHR.2016.0091"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., Shan, Y.: Real-esrgan: training real-world blind super-resolution with pure synthetic data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1905\u20131914 (2021)","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"2_CR33","doi-asserted-by":"publisher","unstructured":"Wu, F.H.F.: An evaluation framework of optical music recognition in numbered music notation. In: 2016 IEEE International Symposium on Multimedia (ISM), pp. 626\u2013631 (2016). https:\/\/doi.org\/10.1109\/ISM.2016.0134","DOI":"10.1109\/ISM.2016.0134"},{"key":"2_CR34","unstructured":"Yoyo, Liebhardt, C., Samuel, S.: Breezewhite\/oemer: v0.1.7 (2023)"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04624-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:34:43Z","timestamp":1758000883000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04624-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"ISBN":["9783032046239","9783032046246"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04624-6_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,17]]},"assertion":[{"value":"17 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icdar2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}