{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:31:33Z","timestamp":1769848293922,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819794331","type":"print"},{"value":"9789819794348","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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\/978-981-97-9434-8_7","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:03:04Z","timestamp":1730383384000},"page":"82-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TF-FusNet: A Novel Framework for\u00a0Parkinson\u2019s Disease Detection via\u00a0Time-Frequency Domain Fusion"],"prefix":"10.1007","author":[{"given":"Tao","family":"Ren","sequence":"first","affiliation":[]},{"given":"Weijie","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Aite","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Aftab, A., Morsali, A., Ghaemmaghami, S., Champagne, B.: Light-sernet: a lightweight fully convolutional neural network for speech emotion recognition. In: ICASSP 2022-2022. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746679"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Demir, F., Sengur, A., Ari, A., Siddique, K., Alswaitti, M.: Feature mapping and deep long short term memory network-based efficient approach for Parkinson\u2019s disease diagnosis. IEEE Access (2021)","DOI":"10.1109\/ACCESS.2021.3124765"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Dewi, S.P., Prasasti, A.L., Irawan, B.: Analysis of IFCC feature extraction in baby crying classification using KNN. In: 2019 IEEE International Conference on IoTaIS. IEEE (2019)","DOI":"10.1109\/IoTaIS47347.2019.8980389"},{"key":"7_CR4","unstructured":"Dimauro, G., Girardi, F.: Italian Parkinson\u2019s voice and speech (2019)"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Eyigoz, E., et\u00a0al.: From discourse to pathology: Automatic identification of Parkinson\u2019s disease patients via morphological measures across three languages. Cortex (2020)","DOI":"10.1016\/j.cortex.2020.08.020"},{"key":"7_CR6","unstructured":"Filippi, M., et\u00a0al.: Longitudinal brain connectivity changes and clinical evolution in Parkinson\u2019s disease. Molecular Psychiatry (2021)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Guo, J., et al.: CMT: convolutional neural networks meet vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01186"},{"issue":"8","key":"7_CR8","doi-asserted-by":"publisher","first-page":"e0267132","DOI":"10.1371\/journal.pone.0267132","volume":"17","author":"Y Guo","year":"2022","unstructured":"Guo, Y., Xiong, X., Liu, Y., Xu, L., Li, Q.: A novel speech emotion recognition method based on feature construction and ensemble learning. PLoS ONE 17(8), e0267132 (2022)","journal-title":"PLoS ONE"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Gupta, H., Gupta, D.: LPC and LPCC method of feature extraction in speech recognition system. In: 2016 6th International Conference-Cloud System and Big Data Engineering. IEEE (2016)","DOI":"10.1109\/CONFLUENCE.2016.7508171"},{"key":"7_CR10","unstructured":"Jaeger, H., Trivedi, D., Stadtschnitzer, M.: Mobile device voice recordings at King\u2019s College London (MDVR-KCL) from both early and advanced Parkinson\u2019s disease patients and healthy controls. Zenodo (2019)"},{"key":"7_CR11","unstructured":"Latif, S., et\u00a0al.: Dopamine in Parkinson\u2019s disease. Clin. Chim. Acta (2021)"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Moro-Velazquez, L., Gomez-Garcia, J.A., Arias-Londo\u00f1o, J.D., Dehak, N., Godino-Llorente, J.I.: Advances in Parkinson\u2019s disease detection and assessment using voice and speech: a review of the articulatory and phonatory aspects. Biomed. Signal Process. Control 66, 102418 (2021)","DOI":"10.1016\/j.bspc.2021.102418"},{"key":"7_CR13","unstructured":"Nasiri, A., Hu, J.: Soundclr: contrastive learning of representations for improved environmental sound classification. arXiv preprint arXiv:2103.01929 (2021)"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Rejaibi, E., Komaty, A., Meriaudeau, F., Agrebi, S., Othmani, A.: MFCC-based recurrent neural network for automatic clinical depression recognition and assessment from speech. Biomed. Signal Process. Control 71, 103107 (2022)","DOI":"10.1016\/j.bspc.2021.103107"},{"key":"7_CR15","unstructured":"Shafeena, M., Vijayan, S.: Parkinson\u2019s disease prognosis using the resnet-50 model from speech features. In: 2022 International Conference on ICISTSD. IEEE (2022)"},{"key":"7_CR16","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-030-95711-7_5","volume-title":"Artificial Intelligence and Speech Technology","author":"A Suresh","year":"2022","unstructured":"Suresh, A., Jain, A., Mathur, K., Gambhir, P.: Comparison of modelling ASR system with different features extraction methods using sequential model. In: Dev, A., Agrawal, S.S., Sharma, A. (eds.) AIST 2021. CCIS, vol. 1546, pp. 47\u201361. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-95711-7_5"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Ye, J.X., et al.: Gm-tcnet: gated multi-scale temporal convolutional network using emotion causality for speech emotion recognition. Speech Communication (2022)","DOI":"10.2139\/ssrn.4055330"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Ye, J., Wen, X.C., Wei, Y., Xu, Y., Liu, K., Shan, H.: Temporal modeling matters: a novel temporal emotional modeling approach for speech emotion recognition. In: ICASSP 2023-2023. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10096370"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, W., Li, X.: Speech emotion recognition with global-aware fusion on multi-scale feature representation. In: ICASSP 2022-2022. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9747517"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-9434-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:35:38Z","timestamp":1730385338000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-9434-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9789819794331","9789819794348"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-9434-8_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2024\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}