Single-Channel Speech Enhancement Based on Psychoacoustic Masking

Zhou T, Zeng Y, Wang R. Single-Channel Speech Enhancement Based on Psychoacoustic Masking. J Audio Eng Soc. 2017;65(4):272-284. doi:10.17743/jaes.2017.0003

@article{Zhou2017_18560,
  author = {Zhou, Tingting and Zeng, Yumin and Wang, Rongrong},
  title = {{Single-Channel Speech Enhancement Based on Psychoacoustic Masking}},
  journal = {Journal of the Audio Engineering Society},
  volume = {65},
  number = {4},
  pages = {272--284},
  year = {2017},
  month = apr,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2017.0003},
  url = {https://doi.org/10.17743/jaes.2017.0003}
}

TY  - JOUR
TI  - Single-Channel Speech Enhancement Based on Psychoacoustic Masking
AU  - Zhou, Tingting
AU  - Zeng, Yumin
AU  - Wang, Rongrong
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 65
IS  - 4
SP  - 272
EP  - 284
PY  - 2017
DA  - 2017/04/06
DO  - 10.17743/jaes.2017.0003
UR  - https://doi.org/10.17743/jaes.2017.0003
PB  - Audio Engineering Society
LA  - en
AB  - Speech enhancement processing can improve the performance of speech communication systems in noisy environments, such as in mobile communication systems, speech recognition, or hearing aids. Single-channel speech enhancement is more difficult than it is with multiple channels since there is no independent source of information that can help separate the speech and noise signals. This paper addresses single-channel speech enhancement based on the masking properties of the human auditory system. A complete implementation of speech enhancement using psychoacoustic masking is presented. The incorporation of temporal masking along with simultaneous masking (as compared to using only simultaneous masking) produces results that are more consistent with human auditory characteristics. The combined masking is then used to adapt the subtraction parameters to obtain the best trade-off among noise reduction, speech distortion, and the level of residual perceptual noise. The application of objective measures and subjective listening tests demonstrate that the proposed algorithm outperforms comparable speech enhancement algorithms.
ER  -