Yang Xiang (项扬)

Education

Aalborg University, Aalborg, Denmark 

Ph.D. student in Electrical and Electronic Engineering,  Sep. 2019 - Jun.2023.

Main supervisor: Prof. Mads Græsbøll Christensen

Co-supervisor: Dr. Morten Højfeldt Rasmussen and Dr. Jesper Lisby Højvang

Beijing University of Technology, Beijing, Chian

M.S. in Information and Communication Engineering, Sep. 2016 - Sep. 2019

Supervisor: Prof. Changchun Bao

B.E. in Electronic Information Engineering, Sep. 2012 - Sep. 2016

Teaching / Professional Experiences

Aalborg University, Aalborg, Denmark

Course lecture: Topics in Audio and Acoustic Signal Processing (2021), Nov. 2021

Project supervisor: Audio processing projects (2021), Feb. 2021 - Jun. 2021

Capturi A/S, Aarhus, Denmark

Industrial Ph.D. student, Sep. 2019 - Feb.2023

Working Experiences

Hithink RoyalFlush AI Research Institute, Hangzhou, China

 Algorithm engineer, Jul. 2023 - present

1. Build a model-based speech enhancement module that supports two task objectives: improving auditory experience and enhancing speech recognition accuracy.

2. Participate in complex scenario speech recognition tasks, utilizing enhancement techniques to optimize solutions and improve recognition accuracy.

3. Explore cutting-edge technologies in speech enhancement, translating relevant research findings into patents or papers.

4. Participate in ICASSP 2024 ICMC-ASR challenge: response for front-end signal processing (Track 2: Rank 2).

Academic Services

Reviewer:

IEEE/ACM Trans. Audio, Speech, and Lang. Process;

ICASSP;

Interspeech;

Eurosipco;

Speech Communication;

 Applied Acoustics; 

SCIENCE CHINA Information Sciences.

Selected Publications

• Y. Xiang, J. Tian, X. Hu, X. Xu, Z. Yin, “A Deep Representation Learning-based Speech Enhancement Method Using Complex Convolution Recurrent Variational Autoencoder”, Accepted by ICASSP 2024. 

• Y. Xiang, J.L Højvang, M.H Rasmussen, and M.G Christensen, “A Two-stage Deep Representation Learning-based Speech Enhancement Method Using Variational Autoencoder and Adversarial Learning.” IEEE/ACM Transactions on Audio, Speech, and Language Processing 32 (2023): 164-177

• S. Tao, Y. Xiang, H. Reddy, J.R Jensen, M.G Christensen, "Single Channel Speech Presence Probability Estimation based on Hybrid Global-Local Information." 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2023.

• Y. Xiang, J.L Højvang, M.H Rasmussen, and M.G Christensen, “A deep representation learning speech enhancement method using β-VAE.” Accepted by Eurosipco 2022.

• Y. Xiang, J.L Højvang, M.H Rasmussen, and M.G Christensen, “A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder.” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process (ICASSP), 2022, pp. 381-385.

• Y. Xiang, L. Shi, J.L Højvang, M.H Rasmussen, and M.G Christensen, “A Novel NMF-HMM Speech Enhancement Algorithm Based on Poisson Mixture Model” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process  (ICASSP)., 2021, pp. 721-725.

• Y. Xiang, L. Shi, J.L Højvang, M.H Rasmussen, and M.G Christensen, “An NMF-HMM speech enhancement method based on kullback-leibler divergence” in Proc. Interspeech,2020, pp. 2667–2671.

• Y. Xiang and C. Bao, “A parallel-data-free speech enhancement method using multi-objective learning cycle-consistent generative adversarial  network”, IEEE/ACM Trans. Audio, Speech, and Lang. Process., vol. 28, pp. 1826–1838, 2020.

• Y. Xiang and C. Bao, “Speech enhancement via generative adversarial LSTM networks”, International Workshop on Acoustic Signal Enhancement (IWAENC), 2018, pp. 46-50.

• Q. Huang, C. Bao, X. Wang, and Y. Xiang, “Speech enhancement method based on multi-band excitation model”, Applied Acoustics., vol. 163, pp. 10236, 2020.

• Q. Huang, C. Bao, X. Wang, and Y. Xiang, “DNN-based speech enhancement using MBE model”, International Workshop on Acoustic Signal Enhancement (IWAENC), 2018, pp. 46-50.