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一种新型助听器反心形指向性模式的临床评估。

Clinical evaluation of a new hearing aid anti-cardioid directivity pattern.

机构信息

Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, USA.

出版信息

Int J Audiol. 2011 Apr;50(4):249-54. doi: 10.3109/14992027.2010.547992. Epub 2011 Jan 27.

Abstract

OBJECTIVE

The purpose of this research was to evaluate a new directional hearing aid algorithm which automatically adapts to an anti-cardioid pattern in background noise when a speech signal originates from behind the hearing aid user.

DESIGN

Using the hearing-in-noise-test (HINT) in the soundfield, with the sentences delivered adaptively from the back (180°) and the standard HINT competing noise from the front (0°; 72 dB SPL), the participants were tested for three different hearing aid conditions: omnidirectional, conventional adaptive directional, and adaptive directional with the anti-cardioid algorithm enabled.

STUDY SAMPLE

Adults (n = 21) with bilaterally symmetrical downward sloping sensorineural hearing loss; experienced hearing aid users and aided bilaterally for experimental testing.

RESULTS

Results revealed a significant effect for the hearing aid microphone setting (p < .0001), with a HINT mean RTS of 4.2 dB for conventional adaptive directional, -0.1 dB for omnidirectional, and -5.7 dB when the anti-cardioid algorithm was active. This was a large effect size (Cohen's f2).

CONCLUSION

The findings suggest that the signal classification system steered the algorithm correctly, and that when implemented, the anti-cardioid polar pattern resulted in an improvement in speech recognition in background noise for this listening situation.

摘要

目的

本研究旨在评估一种新的方向性听力辅助算法,该算法在语音信号源自听力辅助使用者后方的背景噪声中自动适应反心形模式。

设计

使用声场中的噪声下言语测试(HINT),自适应地从前(0°; 72 dB SPL)和标准 HINT 竞争噪声的位置(180°)发送句子,对三种不同的听力辅助条件进行了测试:全向、常规自适应方向性和启用反心形算法的自适应方向性。

研究样本

双侧对称性向下倾斜感音神经性听力损失的成年人(n = 21);双侧有经验的听力辅助使用者,用于实验测试。

结果

结果表明,听力辅助麦克风设置有显著影响(p <.0001),常规自适应方向性的 HINT 平均 RTS 为 4.2 dB,全向性为-0.1 dB,反心形算法激活时为-5.7 dB。这是一个较大的效应量(Cohen's f2)。

结论

研究结果表明,信号分类系统正确引导了算法,并且当实施时,反心形极模式在这种听力情况下的背景噪声中的言语识别方面得到了改善。

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