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Predictive measures of directional benefit part 2: verification of different approaches to estimating directional benefit.

机译:定向效益的预测措施第2部分:验证估算定向效益的不同方法。

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OBJECTIVE: In this investigation, the relation between various directivity measures and subject performance with directional microphone hearing aids was determined. DESIGN: Test devices included first- and second-order directional microphones. Recordings of sentences and noise (Hearing in Noise Test, HINT) were made through each test device in simple, complex, and anisotropic background noise conditions. Twenty-six subjects, with normal hearing, were administered the HINT test recordings and directional benefit was computed. These measures were correlated to theoretical, free-field, and Knowles Electronic Manikin for Acoustic Research (KEMAR) directivity index (DI) values, as well as front-to-back ratios (FBR), in situ signal-to-noise ratios (SNR), and a newly proposed Db SNR, wherein a predictive value of the SNR improvement is calculated as a function of the noise source incidence. RESULTS: The different predictive scores showed high correlation to the measured directional benefit scores in the complex (diffuse-like) background noise condition (r = 0.89 to 0.97, p < 0.05) but not across all background noise conditions (r = 0.45 to 0.97, p < 0.05). The Db SNR approach and the in situ SNR measures provided excellent prediction of subject performance in all background noise conditions (0.85 to 0.97, p < 0.05). None of the predictive measures could account for the effects of reverberation on the speech signal (r = 0.35 to 0.40, p < 0.05). CONCLUSIONS: For environments that included a discrete number of noise sources, the in situ SNR and Db SNR estimates were most predictive of subject performance. No predictive approach was indicative of the directional benefit achieved when the speech was also subjected to reverberation (temporal distortion). This finding has implications for real-world estimates of directional benefit.
机译:目的:在这项调查中,确定了定向麦克风助听器的各种指向性措施与受试者表现之间的关系。设计:测试设备包括一阶和二阶定向麦克风。在简单,复杂和各向异性的背景噪声条件下,通过每个测试设备记录句子和噪声(在噪声测试中听到,提示)。对26名听力正常的受试者进行了HINT测试记录并计算了定向获益。这些措施与理论,自由场和Knowles电子人体模型的声学研究(KEMAR)方向性指数(DI)值以及前后比(FBR),原位信噪比( SNR)和新提出的Db SNR,其中SNR改善的预测值是根据噪声源发生率来计算的。结果:不同的预测分数显示出与复杂(类似扩散)背景噪声条件下的测得的定向效益得分高度相关(r = 0.89至0.97,p <0.05),但并非在所有背景噪声条件下(r = 0.45至0.97) ,p <0.05)。 Db SNR方法和原位SNR测量可在所有背景噪声条件下(0.85至0.97,p <0.05)提供出色的对象性能预测。没有任何一种预测措施能够说明混响对语音信号的影响(r = 0.35至0.40,p <0.05)。结论:对于包括离散数量噪声源的环境,原位SNR和Db SNR估计最能预测受试者的表现。当语音也受到混响(时间失真)时,没有任何预测性方法可以指示所获得的定向益处。这一发现对现实世界中的定向利益估计具有影响。

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