季春艳 - 北京师范大学-香港浸会大学

个人简介

季春艳博士本科毕业于华东师范大学，在美国佐治亚州立大学获得硕士和博士学位。在攻读博士之前，她在美国工业界工作10年，于UIC担任教职10年，曾获第一届UIC校长教学奖和南粤优秀教师荣誉。她现任北师港浸大计算机系助理教授，主要研究方向包括深度学习，生物信息，声音事件检测等领域。奖励与荣誉 2019 最佳论文 in the 5th IEEE International Conference on Smart Data, 2019 2017 广东省精品教材荣誉 2015 广东省南粤优秀教师荣誉 2015 北师大-香港浸会大学联合国际学院校长教学奖

研究领域

机器学习/深度学习声音事件检测/音频信号处理/婴幼儿哭声语音预测生物信息学（疾病预测，药物靶向预测）教学数据挖掘与分析

近期论文

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Ji, C., Jiao, Y., Chen, M., Pan, Y. (2022). Infant Cry Classification Based-On Feature Fusion and Mel-Spectrogram Decomposition with CNNs. In: Pan, X., Jin, T., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2022. AIMS 2022. Lecture Notes in Computer Science, vol 13729. Springer, Cham. https://doi.org/10.1007/978-3-031-23504-7_10 Chunyan Ji, Yi Pan, “Infant Vocal Tract Development Analysis and Diagnosis by Cry Signals with CNN Age Classification”, the 11th Conference on Speech Technology and Human-Computer Dialogue, 2021. Chunyan Ji, Ming Chen, Bin Li, Yi Pan, “Infant Cry Classification with Graph Convolutional Networks,” 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), 2021, pp. 322-327. Chunyan Ji, T.B. Mudiyanselage, Yutong Gao, Yi Pan, “A review of infant cry analysis and classification.” J AUDIO SPEECH MUSIC PROC. 2021, 8 (2021). Chunyan Ji, Sunitha Basodi, Xueli Xiao, Yi Pan, “Infant Sound Classification on Multi-stage CNNs with Hybrid Features and Prior Knowledge”. Artificial Intelligence and Mobile Services – AIMS 2020. AIMS 2020. Lecture Notes in Computer Science, vol 12401. Springer, Cham. Chunyan Ji, Xueli Xiao, Sunitha Basodi, Yi Pan, “Deep Learning for Asphyxiated Infant Cry Classification Based on Acoustic Features and Weighted Prosodic Features”, 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2019, pp. 1233-1240. Ming Chen, Yi Pan, Chunyan Ji, “Predicting Drug Drug Interactions by Signed Graph Filtering-based Convolutional Networks”, International Symposium on Bioinformatics Research and Applications, 2021. Sunitha Basodi, Chunyan Ji, Haiping Zhang and Yi Pan, “Gradient amplification: An efficient way to train deep neural networks”, Big Data Mining and Analytics, vol. 3, no. 3, pp. 196-207, Sept. 2020.