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Shuo Liu

7 papers hereh-index 131k citations32 works total

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

author position
  • first author5
  • middle author2

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.SD4
  • cs.HC1
  • eess.AS1
  • eess.SP1
same name
  • Shuo Liu — 21 papers, h 13
  • Shuo Liu — 15 papers, h 6
  • Shuo Liu — 10 papers, h 8
  • Shuo Liu — 8 papers, h 4
  • Shuo Liu — 7 papers, h 10
  • Shuo Liu — 6 papers, h 13

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
20192023
most citedSingle-Channel Speech Separation with Auxiliary Speaker Embeddings

4 citations · 11 across the 6 of their papers we have counts for

collaborators
Showing cs.SDShow all

4 papers · 1 filter

cs.SD2022

A Temporal-oriented Broadcast ResNet for COVID-19 Detection

Xin Jing, Shuo Liu, Emilia Parada-Cabaleiro +4

Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission. Due to the promising…

cs.SD2022★ 1 cited

Audio Self-supervised Learning: A Survey

Shuo Liu, Adria Mallol-Ragolta, Emilia Parada-Cabeleiro +5

Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data wi…

cs.SD2019★ 4 cited

N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System

Shuo Liu, Gil Keren, Björn Schuller

N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression. The funct…

cs.SD2019★ 4 cited

Single-Channel Speech Separation with Auxiliary Speaker Embeddings

Shuo Liu, Gil Keren, Björn Schuller

We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers. The proposed model is a neural n…

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