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正常听力和听力受损听众中稀疏编码收缩降噪算法的评估

Evaluation of the sparse coding shrinkage noise reduction algorithm in normal hearing and hearing impaired listeners.

作者信息

Sang Jinqiu, Hu Hongmei, Zheng Chengshi, Li Guoping, Lutman Mark E, Bleeck Stefan

机构信息

Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, UK.

Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

出版信息

Hear Res. 2014 Apr;310:36-47. doi: 10.1016/j.heares.2014.01.006. Epub 2014 Feb 2.

Abstract

Although there are numerous single-channel noise reduction strategies to improve speech perception in noise, most of them improve speech quality but do not improve speech intelligibility, in circumstances where the noise and speech have similar frequency spectra. Current exceptions that may improve speech intelligibility are those that require a priori knowledge of the speech or noise statistics, which limits practical application. Hearing impaired (HI) listeners suffer more in speech intelligibility than normal hearing listeners (NH) in the same noisy environment, so developing better single-channel noise reduction algorithms for HI listeners is justified. Our model-based "sparse coding shrinkage" (SCS) algorithm extracts key speech information in noisy speech. We evaluate it by comparison with a state-of-the-art Wiener filtering approach using speech intelligibility tests with NH and HI listeners. The model-based SCS algorithm relies only on statistical signal information without prior information. Results show that the SCS algorithm improves speech intelligibility in stationary noise and is comparable to the Wiener filtering algorithm. Both algorithms improve intelligibility for HI listeners but not for NH listeners. Improvement is less in fluctuating (babble) noise than in stationary noise. Both noise reduction algorithms perform better at higher input signal-to-noise ratios (SNR) where HI listeners can benefit but where NH listeners have already reached ceiling performance. The difference between NH and HI subjects in intelligibility gain depends fundamentally on the input SNR rather than the hearing loss level. We conclude that HI listeners need different signal processing algorithms from NH subjects and that the SCS algorithm offers a promising alternative to Wiener filtering. Performance of all noise reduction algorithms is likely to vary according to extent of hearing loss and algorithms that show little benefit for listeners with moderate hearing loss may be more beneficial for listeners with more severe hearing loss.

摘要

尽管有众多单通道降噪策略来提高噪声环境下的言语感知,但在噪声和语音具有相似频谱的情况下,它们大多改善了语音质量却未提高言语清晰度。当前可能提高言语清晰度的例外情况是那些需要语音或噪声统计的先验知识的策略,这限制了实际应用。在相同的嘈杂环境中,听力受损(HI)的听众比听力正常(NH)的听众在言语清晰度方面受到的影响更大,因此为HI听众开发更好的单通道降噪算法是合理的。我们基于模型的“稀疏编码收缩”(SCS)算法可从嘈杂语音中提取关键语音信息。我们通过与一种使用NH和HI听众进行言语清晰度测试的先进维纳滤波方法进行比较来评估它。基于模型的SCS算法仅依赖统计信号信息而无需先验信息。结果表明,SCS算法在平稳噪声中提高了言语清晰度,并且与维纳滤波算法相当。两种算法都提高了HI听众的清晰度,但对NH听众没有效果。在波动(嘈杂)噪声中的改善不如在平稳噪声中明显。两种降噪算法在较高的输入信噪比(SNR)下表现更好,此时HI听众可以受益,但NH听众已经达到了最高性能。NH和HI受试者在清晰度增益方面的差异从根本上取决于输入SNR,而不是听力损失程度。我们得出结论,HI听众需要与NH受试者不同的信号处理算法,并且SCS算法为维纳滤波提供了一个有前景的替代方案。所有降噪算法的性能可能会根据听力损失程度而有所不同,对于中度听力损失的听众几乎没有益处的算法可能对重度听力损失的听众更有益。

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