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Speech quality evaluation of a sparse coding shrinkage noise reduction algorithm with normal hearing and hearing impaired listeners.
Hear Res. 2015 Sep; 327:175-85.HR

Abstract

Although there are numerous papers describing single-channel noise reduction strategies to improve speech perception in a noisy environment, few studies have comprehensively evaluated the effects of noise reduction algorithms on speech quality for hearing impaired (HI). A model-based sparse coding shrinkage (SCS) algorithm has been developed, and has shown previously (Sang et al., 2014) that it is as competitive as a state-of-the-art Wiener filter approach in speech intelligibility. Here, the analysis is extended to include subjective quality ratings and a method called Interpolated Paired Comparison Rating (IPCR) is adopted to quantitatively link the benefit of speech intelligibility and speech quality. The subjective quality tests are performed through IPCR to efficiently quantify noise reduction effects on speech quality. Objective measures including frequency-weighted segmental signal-to-noise ratio (fwsegSNR), perceptual evaluation of speech quality (PESQ) and hearing aid speech quality index (HASQI) are adopted to predict the noise reduction effects. Results show little difference in speech quality between the SCS and the Wiener filter algorithm but a difference in quality rating between the HI and NH listeners. HI listeners generally gave better quality ratings of noise reduction algorithms than NH listeners. However, SCS reduced the noise more efficiently at the cost of higher distortions that were detected by NH but not by the HI. SCS is a promising candidate for noise reduction algorithms for HI. In general, care needs to be taken when adopting algorithms that were originally developed for NH participants into hearing aid applications. An algorithm that is evaluated negatively with NH might still bring benefits for HI participants.

Authors+Show Affiliations

Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, UK; Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, UK.Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, UK.Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, UK.Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, UK. Electronic address: s.bleeck@soton.ac.uk.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26232529

Citation

Sang, Jinqiu, et al. "Speech Quality Evaluation of a Sparse Coding Shrinkage Noise Reduction Algorithm With Normal Hearing and Hearing Impaired Listeners." Hearing Research, vol. 327, 2015, pp. 175-85.
Sang J, Hu H, Zheng C, et al. Speech quality evaluation of a sparse coding shrinkage noise reduction algorithm with normal hearing and hearing impaired listeners. Hear Res. 2015;327:175-85.
Sang, J., Hu, H., Zheng, C., Li, G., Lutman, M. E., & Bleeck, S. (2015). Speech quality evaluation of a sparse coding shrinkage noise reduction algorithm with normal hearing and hearing impaired listeners. Hearing Research, 327, 175-85. https://doi.org/10.1016/j.heares.2015.07.019
Sang J, et al. Speech Quality Evaluation of a Sparse Coding Shrinkage Noise Reduction Algorithm With Normal Hearing and Hearing Impaired Listeners. Hear Res. 2015;327:175-85. PubMed PMID: 26232529.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Speech quality evaluation of a sparse coding shrinkage noise reduction algorithm with normal hearing and hearing impaired listeners. AU - Sang,Jinqiu, AU - Hu,Hongmei, AU - Zheng,Chengshi, AU - Li,Guoping, AU - Lutman,Mark E, AU - Bleeck,Stefan, Y1 - 2015/07/29/ PY - 2014/12/24/received PY - 2015/07/20/revised PY - 2015/07/23/accepted PY - 2015/8/2/entrez PY - 2015/8/2/pubmed PY - 2016/6/9/medline KW - Hearing impairment KW - Interpolated paired comparison rating KW - Noise reduction KW - Normal hearing KW - Sparse coding shrinkage KW - Speech quality SP - 175 EP - 85 JF - Hearing research JO - Hear Res VL - 327 N2 - Although there are numerous papers describing single-channel noise reduction strategies to improve speech perception in a noisy environment, few studies have comprehensively evaluated the effects of noise reduction algorithms on speech quality for hearing impaired (HI). A model-based sparse coding shrinkage (SCS) algorithm has been developed, and has shown previously (Sang et al., 2014) that it is as competitive as a state-of-the-art Wiener filter approach in speech intelligibility. Here, the analysis is extended to include subjective quality ratings and a method called Interpolated Paired Comparison Rating (IPCR) is adopted to quantitatively link the benefit of speech intelligibility and speech quality. The subjective quality tests are performed through IPCR to efficiently quantify noise reduction effects on speech quality. Objective measures including frequency-weighted segmental signal-to-noise ratio (fwsegSNR), perceptual evaluation of speech quality (PESQ) and hearing aid speech quality index (HASQI) are adopted to predict the noise reduction effects. Results show little difference in speech quality between the SCS and the Wiener filter algorithm but a difference in quality rating between the HI and NH listeners. HI listeners generally gave better quality ratings of noise reduction algorithms than NH listeners. However, SCS reduced the noise more efficiently at the cost of higher distortions that were detected by NH but not by the HI. SCS is a promising candidate for noise reduction algorithms for HI. In general, care needs to be taken when adopting algorithms that were originally developed for NH participants into hearing aid applications. An algorithm that is evaluated negatively with NH might still bring benefits for HI participants. SN - 1878-5891 UR - https://www.unboundmedicine.com/medline/citation/26232529/Speech_quality_evaluation_of_a_sparse_coding_shrinkage_noise_reduction_algorithm_with_normal_hearing_and_hearing_impaired_listeners_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0378-5955(15)00159-8 DB - PRIME DP - Unbound Medicine ER -