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- 2026
[j6]Ye Ni
, Ruiyu Liang, Xiaoshuai Hao, Jiaming Cheng, Qingyun Wang, Chengwei Huang, Cairong Zou, Wei Zhou, Weiping Ding, Björn W. Schuller:
Affine modulation-based audiogram fusion network for joint noise reduction and hearing loss compensation. Inf. Fusion 127: 103726 (2026)- [i3]Changsong Liu, Tianrui Wang, Ye Ni, Yizhou Peng, Eng Siong Chng:
Prosodic Boundary-Aware Streaming Generation for LLM-Based TTS with Streaming Text Input. CoRR abs/2603.06444 (2026) - 2025
- [j5]Cong Pang, Ye Ni, Lin Zhou, Li Zhao, Feifei Xiong:
Exploiting spatial information and target speaker phoneme loss for multichannel directional speech enhancement and recognition. Comput. Speech Lang. 93: 101801 (2025) - [j4]Cong Pang, Ye Ni, Jiaming Cheng, Lin Zhou, Li Zhao:
BP-CRN: A Lightweight Two-Stage Convolutional Recurrent Network for Multi-Channel Speech Enhancement. IEICE Trans. Inf. Syst. 108(2): 161-164 (2025) - [j3]MinJie Xiang, Ruiyu Liang, Ye Ni, Li Zhao, Björn W. Schuller:
Lightweight Attentive ConvNeXt-TCN for Causal Target Sound Extraction. IEEE Signal Process. Lett. 32: 4234-4238 (2025) - [i2]Jiaming Cheng, Ruiyu Liang, Chao Xu, Ye Ni, Wei Zhou, Björn W. Schuller, Xiaoshuai Hao:
I2S-TFCKD: Intra-Inter Set Knowledge Distillation with Time-Frequency Calibration for Speech Enhancement. CoRR abs/2506.13127 (2025) - [i1]Haoyang Li, Xuyi Zhuang, Azmat Adnan, Ye Ni, Wei Rao, Shreyas Gopal, Eng Siong Chng:
GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model. CoRR abs/2512.20978 (2025) - 2024
- [c1]Ye Ni, Cong Pang, Chengwei Huang, Cairong Zou:
MSA-DPCRN: A Multi-Scale Asymmetric Dual-Path Convolution Recurrent Network with Attentional Feature Fusion for Acoustic Echo Cancellation. INTERSPEECH 2024 - 2021
- [j2]Ye Ni, Zhilong Xia, Fangtong Zhao, Chunrong Fang, Zhenyu Chen:
An Online Multistep-Forward Voltage-Prediction Approach Based on an LSTM-TD Model and KF Algorithm. Computer 54(8): 56-65 (2021) - [j1]Ye Ni, Xutao Li, Yunming Ye, Yan Li, Chunshan Li, Dianhui Chu:
An Investigation on Deep Learning Approaches to Combining Nighttime and Daytime Satellite Imagery for Poverty Prediction. IEEE Geosci. Remote. Sens. Lett. 18(9): 1545-1549 (2021)
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