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Junfeng Li

4 papers hereh-index 17770 citations119 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.SD3
  • cs.NI1
same name
  • Junfeng Li — 13 papers, h 32
  • Junfeng Li — 8 papers, h 3
  • Junfeng Li — 7 papers, h 4
  • Junfeng Li — 5 papers, h 9
  • Junfeng Li — 4 papers, h 5
  • Junfeng Li — 3 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202026
most citedA Model Compression Method with Matrix Product Operators for Speech Enhancement

15 citations · 18 across the 3 of their papers we have counts for

collaborators

4 papers

cs.SD2026

Noise-Aware Lombard Speech Synthesis via Flow-GRPO for Intelligibility Optimization

Zhaoyang Wang, Dingding Yao, Chengzhong Wang +1

Voice-based conversational agents are increasingly deployed in real-world settings, where environmental noise often degrades the interactive experience. Therefore, robust acoustic…

cs.SD2021

A Novel Blind Source Separation Framework Towards Maximum Signal-To-Interference Ratio

Jianju Gu, Longbiao Cheng, Dingding Yao +2

This letter proposes a new blind source separation (BSS) framework termed minimum variance independent component analysis (MVICA), which can potentially achieve the maximum output…

cs.NI2020★ 3 cited

Compressing LSTM Networks by Matrix Product Operators

Ze-Feng Gao, Xingwei Sun, Lan Gao +2

Long Short Term Memory(LSTM) models are the building blocks of many state-of-the-art natural language processing(NLP) and speech enhancement(SE) algorithms. However, there are a la…

cs.SD2020★ 15 cited

A Model Compression Method with Matrix Product Operators for Speech Enhancement

Xingwei Sun, Ze-Feng Gao, Zhong-Yi Lu +2

The deep neural network (DNN) based speech enhancement approaches have achieved promising performance. However, the number of parameters involved in these methods is usually enormo…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.