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Jialu Li

12 papers hereh-index 434 citations18 works total

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

author position
  • first author2
  • middle author10

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.SD5
  • eess.IV1
same name
  • Jialu Li — 16 papers, h 13
  • Jialu Li — 11 papers, h 6
  • Jialu Li — 6 papers, h 6
  • Jialu Li — 5 papers, h 2
  • Jialu Li — 4 papers, h 2
  • Jialu Li — 2 papers, h 2

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
20232026
collaborators
Showing cs.SDShow all

5 papers · 1 filter

cs.SD2024

Vision Transformer Segmentation for Visual Bird Sound Denoising

Sahil Kumar, Jialu Li, Youshan Zhang

Audio denoising, especially in the context of bird sounds, remains a challenging task due to persistent residual noise. Traditional and deep learning methods often struggle with ar…

cs.SD2024

Complex Image-Generative Diffusion Transformer for Audio Denoising

Junhui Li, Pu Wang, Jialu Li +1

The audio denoising technique has captured widespread attention in the deep neural network field. Recently, the audio denoising problem has been converted into an image generation…

cs.SD2024

Diffusion Gaussian Mixture Audio Denoise

Pu Wang, Junhui Li, Jialu Li +2

Recent diffusion models have achieved promising performances in audio-denoising tasks. The unique property of the reverse process could recover clean signals. However, the distribu…

cs.SD2023

DCHT: Deep Complex Hybrid Transformer for Speech Enhancement

Jialu Li, Junhui Li, Pu Wang +1

Most of the current deep learning-based approaches for speech enhancement only operate in the spectrogram or waveform domain. Although a cross-domain transformer combining waveform…

cs.SD2023

DPATD: Dual-Phase Audio Transformer for Denoising

Junhui Li, Pu Wang, Jialu Li +2

Recent high-performance transformer-based speech enhancement models demonstrate that time domain methods could achieve similar performance as time-frequency domain methods. However…

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