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CHEN Jing

  • Research Professor, Investigator
  • Department of Machine Intelligence, National Biomedical Imaging Center (jointly operated)
  • Peking University, Beijing, China
  • 个人简介
    Mainly focus on three research directions: 1) Auditory attention decoding: the selective attention control in the human brain enables the enhancement of target sounds and suppression of interfering sounds in complex auditory scenes. However, current hearing devices cannot effectively recognize the auditory attention objects of the wearers. Auditory attention decoding aims to decode the objects that listeners are attending to through neural responses (EEG, MEG, ECoG, etc.). The current mainstream method is to reconstruct various auditory cues (such as envelope, onset, pitch, etc.) from neural signals using linear or nonlinear (neural network) methods, and there are also studies that use end-to-end neural networks to directly decode attention objects. 2) Semantic decoding: semantic concepts are the basis of human thinking and understanding of the world, and researchers have found that specific semantic concepts have specific neural activity patterns. Based on this, people began to explore semantic concept decoding, which is to identify the semantic concepts that participants are thinking about or focusing on from neural signals. In this problem, researchers use audio, images, text, etc. as stimuli to guide participants to think about or focus on specific semantic concepts, and record the participants' neural signals (fMRI, MEG, EEG, etc.) during the process. In the decoding process, various methods such as signal processing and machine learning are used to extract features from neural signals and construct decoders from neural signals to semantic concepts to achieve decoding of semantic concepts. 3) Intelligent hearing aids: hearing loss is one of the important public health problems, and if it is not effectively intervened, it will cause communication barriers for patients and affect their daily life and communication. Providing appropriate hearing rehabilitation services can compensate for the hearing loss of the vast majority of hearing loss patients, thereby improving their language ability and quality of life. However, traditional hearing compensation technologies cannot provide personalized hearing compensation for patients and the fitting process is time-consuming and laborious. Therefore, we aim to combine artificial intelligence application technology with audiology to finely model the hearing condition of patients and provide them with personalized hearing compensation and adaptive fitting services.
  • 所授课程
    Brain and cognitive science (Compulsory, 2 credits)

    Auditory information processing (Compulsory, 4 credits)
  • 获奖及荣誉
    "Newton International Fellowship" by Royal Society, UK, 2009
  • 个人履历
    Jing received her Ph.D degree from Peking University in signal and information processing in 2009. She worked as a Newton International Fellow, and a research associate with Prof. Brian Moore at the hearing laboratory of Department of Experimental Psychology of University of Cambridge, UK, in 2009-2012. She was a research fellow of Wolfson College at University of Cambridge, UK. She joined Department of Machine Intelligence of Peking University as a research professor in 2013.
  • 承担项目
    Major Project of the Ministry of Science and Technology 2030 (2021ZD0201500), "Research on Brain-Like Auditory Front-End Models and Systems", Principal Investigator (ongoing).
    Natural Science Foundation of China Project, "Research on Hearing Aid Methods Based on Auditory Attention Detection", Principal Investigator (ongoing).
    Sonova Holding AG (Switzerland), "Chinese Auditory and Artificial Intelligence", Principal Investigator (ongoing).
    Peking University Medical and Information Cross-Project, "Clinical Typing and Intervention Strategies for Age-Related Hearing Loss", Collaborative Principal Investigator (completed).
    National Natural Science Foundation of China Project, "Study on Audio-Visual Dual-Modal Stimulation Methods and Models Based on Speech Intelligibility Theory", Principal Investigator (completed).
    Sonova Hearing Technology (Shanghai) Co., Ltd., "Spectrogram Enhancement Algorithm Based on Deep Neural Networks", Principal Investigator (completed).
    Sonova Hearing Technology (Shanghai) Co., Ltd., "Effects of Spectral Change Enhancement on Speech Intelligibility in Mandarin Chinese", Principal Investigator (completed).
    National Natural Science Foundation of China Project, "Evaluation Methods and Computational Models for Hearing Damage", Principal Investigator (completed).
    Peking University 985 Project Construction Task (One Hundred Talents Plan), "Research on Auditory Noise Resistance Mechanisms and Hearing Rehabilitation Technology", Principal Investigator (completed).
    Follow-on Funding for Newton Alumni, The Royal Academy of Engineering, UK, Principal Investigator (completed).
  • 代表性论文及论著
    1. Li, X (PhD student), Sun, Y., Wu, X., Chen, J.* (2022). Multi-Speaker Pitch Tracking via Embodied Self-Supervised Learning, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, pp. 8257–8261. Singapore City, Singapore.

    2. Fu, Z. (PhD student), Wang, B., Chen, F., Wu, X., Chen, J.* (2021). Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks, 29th European Signal Processing Conference, EUSIPCO 2021, pp. 1261-1265, Dublin, Ireland.

    3. Fu, Z. (PhD student), Wang, B., Wu, X., Chen, J.* (2021). Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network, 29th European Signal Processing Conference, EUSIPCO 2021, pp. 970-974, Dublin, Ireland.
    4. Niu Y (PHD Student), Liu Y, Wu X, Chen, J.* (2021). Categorical perception of lexical tones based on acoustic-electric stimulation. JASA Express Letters, 1(8):084405.
    5. Fu, Z. (PhD student), Chen, J.* (2020). Congruent Audiovisual Speech Enhances Cortical Envelope Tracking during Auditory Selective Attention, 21th Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, pp. 116–120, Shanghai, China.
    6. Li, X. (PhD student), Liu, R., Song, T., Wu, X., Chen, J. * (2020). Single-Channel Speech Separation Integrating Pitch Information Based on a Multi Task Learning Framework, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, pp. 7279-7283, Barcelona, Spain.
    7. Fu, Z. (PhD student), Wu, X., Chen, J.*(2019). Congruent audiovisual speech enhances auditory attention decoding with EEG, Journal of Neural Engineering, 16(6),066033.
    8. Du, Y. (Master student), Shen, Y., Wu, X., Chen, J. *(2019). The effect of speech material on the band importance function for Mandarin Chinese. Journal of the Acoustical Society of America,146(1):445-457.
    9. Fu, Z(PhD student), Yang, H., Chen, F., Wu, X., Chen, J. *(2019) Brainstem encoding of frequency-modulated sweeps is relevant to Mandarin concurrent-vowels identification for normal-hearing and hearing-impaired listeners. Hearing Research, 380:123-136.
    10. Niu, Y. (PHD Student), Chen, F., Chen, J. *(2019) The effect of F0 contour on the intelligibility of Mandarin Chinese for hearing impaired listeners. Journal of the Acoustical Society of America, 146(2):EL85-EL91.
    11. Fu, Z.(PhD student), Wu, X., Chen, J.*(2019) Contribution of spectral and tonal cues to Mandarin concurrent-vowels identification for normal-hearing and hearing-impaired Listeners, in Proceedings of Interspeech 2019, pp. 3083-3087, Graz, Austria, Sep 15-19, 2019.
    12. Li, X. (PhD Student), Wu, X., Chen, J.* (2019) Integrating Spectro-temporal Context into Features Based on Auditory Perception for Classification-based Speech Separation, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7165-7169, Brighton, United Kingdom, May 12-17, 2019.
    13. Li, X. (PhD Student), Wu, X., Chen, J.* (2019) A Spectral-change-aware Loss Function for DNN-based Speech Separation, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6870-6874, Brighton, United Kingdom, May 12-17, 2019.
    14. Chen, J.*, Yang, H.Y., Wu, X.H., & Moore, B.C.J. (2018). The effect of F0 contour on the intelligibility of speech in the presence of interfering sounds for Mandarin Chinese. The Journal of the Acoustical Society of America, 143(2), 864-877.
    15. Chen, J.*, Moore, B.C.J., Baer, T., & Wu, X.H., (2018). Individually tailored spectral-change enhancement for the hearing impaired. The Journal of the Acoustical Society of America, 143(2), 1128-1137.
    16. Du, Y.F. (Master student), Shen, Y., Yang, H.Y., Wu, X.H., Chen, J.* (2018). Measuring the band importance function for Mandarin Chinese with a bayesian adaptive procedure. In Proceedings of Interspeech, pp. 961-965, Hyderabad, India, September 2-6, 2018.
    17. Song, M.J. (Master student), Chen, F., Wu, X.H., Chen, J.* (2018). A time-weighted method for predicting the intelligibility of speech in the presence of interfering sounds. In Proceedings of International Conference of Acoustics, Speech and Signal Processing (ICASSP), pp. 5589-5593, Alberta, Canada, April 15-20, 2018.
    18. Chen, J., Huang, Q., & Wu, X.* (2016). Frequency importance function of the speech intelligibility index for Mandarin Chinese. Speech Communication, 83, 94-103.
    19. He, W., Ding, X., Zhang, R., Chen, J.*, Zhang, D., & Wu, X.* (2014). Electrically-Evoked Frequency-Following Response (EFFR) in the Auditory Brainstem of Guinea Pigs. PloS one, 9(9), e106719, 1-10.
    20. Chen, J.*, Baer, T., & Moore, B. C. (2013). Effect of spectral change enhancement for the hearing impaired using parameter values selected with a genetic algorithm. The Journal of the Acoustical Society of America, 133(5), 2910-2920.
    21. Chen, J., Moore, B.C.J.* (2013) “Effect of individually tailored spectral change enhancement on speech intelligibility and quality for hearing-impaired listeners”, in Proceedings of ICASSP 2013, pp. 8643-8647, Vancouver, Canada, May 2013.
    22. Chen, J.*, Baer, T., and Moore, B. C. J. (2012) Effect of enhancement of spectral changes on speech intelligibility and clarity preferences for the hearing impaired. Journal of Acoustic Society of America, 131(4), 2987-2998.
    23. Chen, J., Li, H.H., Li, L., Moore, B.C.J., Wu, X-H.* (2012), “Informational masking of speech produced by speech-like sounds without linguistic content,” Journal of Acoustic Society of America, 131(4), 2914-2926.
    24. Chen, J.*, Baer, T., and Moore, B. C. J. (2010), “Effects of enhancement of spectral changes on speech quality and subjective speech intelligibility,” in Proceedings of Interspeech 2010, pp. 1640-1643, Tokyo, Japan, October 2010.
    25. Wu, X.H.*, Chen, J.*, Yang, Z.G., Huang, Q., Wang, M.Y., Li, L. (2007), “Effect of number of masking talkers on speech-on-speech masking in Chinese,” in Proceedings of Interspeech 2007, pp. 390-393, Antwerp, Belgium, September 2007.