Speech Enhancement with Nonstationary Acoustic Noise Detection in Time Domain

被引:23
|
作者
Tavares, R. [1 ]
Coelho, R. [1 ]
机构
[1] Mil Inst Engn IME, Lab Acoust Signal Proc, Rio De Janeiro, RJ, Brazil
关键词
Index of nonstationarity; robust estimation; speech enhancement; SPECTRUM; ENVIRONMENTS; RECOGNITION;
D O I
10.1109/LSP.2015.2495102
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This letter proposes a new time domain speech enhancement technique for signals corrupted by nonstationary acoustic noises. In this method, the noise components are detected and attenuated directly from the corrupted speech samples. They are obtained with a robust estimation of the noise standard deviation considering any speech and noise amplitude distribution. These values are used to define a noise selection threshold. Additionally, this solution does not require the usage of any spectral analysis or temporal decomposition as a pre-processing phase. The experiments results show that the proposed scheme leads to significant improvement in the speech quality and intelligibility when compared to competing enhancement approaches.
引用
收藏
页码:6 / 10
页数:5
相关论文
共 50 条
  • [31] The Theory of Compressive Sensing Matching Pursuit Considering Time-domain Noise with Application to Speech Enhancement
    Wu, Dalei
    Zhu, Wei-Ping
    Swamy, M. N. S.
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2014, 22 (03) : 682 - 696
  • [32] Harmonic beamformers for speech enhancement and dereverberation in the time domain
    Jensen, J. R.
    Karimian-Azari, S.
    Christensen, M. G.
    Benesty, J.
    SPEECH COMMUNICATION, 2020, 116 : 1 - 11
  • [33] A New Framework for Supervised Speech Enhancement in the Time Domain
    Pandey, Ashutosh
    Wang, Deliang
    19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES, 2018, : 1136 - 1140
  • [34] A NOISE PREDICTION AND TIME-DOMAIN SUBTRACTION APPROACH TO DEEP NEURAL NETWORK BASED SPEECH ENHANCEMENT
    Odelowo, Babafemi O.
    Anderson, David V.
    2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2017, : 372 - 377
  • [35] Visually Assisted Time-Domain Speech Enhancement
    Ideli, Elham
    Sharpe, Bruce
    Bajic, Ivan, V
    Vaughan, Rodney G.
    2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP), 2019,
  • [36] Neural speech enhancement in the time-frequency domain
    Volkmer, M
    2003 IEEE XIII WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING - NNSP'03, 2003, : 617 - 626
  • [37] Compressed domain noise reduction and echo suppression for network speech enhancement
    Chandran, R
    Marchok, DJ
    PROCEEDINGS OF THE 43RD IEEE MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS I-III, 2000, : 10 - 13
  • [38] SPEECH ENHANCEMENT WITH MASKING PROPERTIES IN EIGEN-DOMAIN FOR COLORED NOISE
    You, Chang Huai
    Lee, Kong Aik
    Leung, Cheung Chi
    2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2011, : 4748 - 4751
  • [39] Joint Time-Frequency and Time Domain Learning for Speech Enhancement
    Tang, Chuanxin
    Luo, Chong
    Zhao, Zhiyuan
    Xie, Wenxuan
    Zeng, Wenjun
    PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, : 3816 - 3822
  • [40] Time domain speech enhancement with CNN and time-attention transformer
    Saleem, Nasir
    Gunawan, Teddy Surya
    Dhahbi, Sami
    Bourouis, Sami
    DIGITAL SIGNAL PROCESSING, 2024, 147