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人工耳蜗中的麦克风指向性、预加重滤波器和风声噪声。

Microphone directionality, pre-emphasis filter, and wind noise in cochlear implants.

作者信息

Chung King, McKibben Nicholas

出版信息

J Am Acad Audiol. 2011 Oct;22(9):586-600. doi: 10.3766/jaaa.22.9.4.

DOI:10.3766/jaaa.22.9.4
PMID:22192604
Abstract

BACKGROUND

Wind noise can be a nuisance or a debilitating masker for cochlear implant users in outdoor environments. Previous studies indicated that wind noise at the microphone/hearing aid output had high levels of low-frequency energy and the amount of noise generated is related to the microphone directionality. Currently, cochlear implants only offer either directional microphones or omnidirectional microphones for users at-large. As all cochlear implants utilize pre-emphasis filters to reduce low-frequency energy before the signal is encoded, effective wind noise reduction algorithms for hearing aids might not be applicable for cochlear implants.

PURPOSE

The purposes of this study were to investigate the effect of microphone directionality on speech recognition and perceived sound quality of cochlear implant users in wind noise and to derive effective wind noise reduction strategies for cochlear implants.

RESEARCH DESIGN

A repeated-measure design was used to examine the effects of spectral and temporal masking created by wind noise recorded through directional and omnidirectional microphones and the effects of pre-emphasis filters on cochlear implant performance. A digital hearing aid was programmed to have linear amplification and relatively flat in-situ frequency responses for the directional and omnidirectional modes. The hearing aid output was then recorded from 0 to 360° at flow velocities of 4.5 and 13.5 m/sec in a quiet wind tunnel.

STUDY SAMPLE

Sixteen postlingually deafened adult cochlear implant listeners who reported to be able to communicate on the phone with friends and family without text messages participated in the study.

INTERVENTION

Cochlear implant users listened to speech in wind noise recorded at locations that the directional and omnidirectional microphones yielded the lowest noise levels.

DATA COLLECTION AND ANALYSIS

Cochlear implant listeners repeated the sentences and rated the sound quality of the testing materials. Spectral and temporal characteristics of flow noise, as well as speech and/or noise characteristics before and after the pre-emphasis filter, were analyzed. Correlation coefficients between speech recognition scores and crest factors of wind noise before and after pre-emphasis filtering were also calculated.

RESULTS

Listeners obtained higher scores using the omnidirectional than the directional microphone mode at 13.5 m/sec, but they obtained similar speech recognition scores for the two microphone modes at 4.5 m/sec. Higher correlation coefficients were obtained between speech recognition scores and crest factors of wind noise after pre-emphasis filtering rather than before filtering.

CONCLUSION

Cochlear implant users would benefit from both directional and omnidirectional microphones to reduce far-field background noise and near-field wind noise. Automatic microphone switching algorithms can be more effective if the incoming signal were analyzed after pre-emphasis filters for microphone switching decisions.

摘要

背景

风噪声对于户外环境中的人工耳蜗使用者来说可能是一种干扰,或者是一种使人衰弱的掩蔽声。先前的研究表明,麦克风/助听器输出处的风噪声具有高水平的低频能量,并且产生的噪声量与麦克风的方向性有关。目前,人工耳蜗通常只为大多数用户提供定向麦克风或全向麦克风。由于所有人工耳蜗在信号编码之前都利用预加重滤波器来降低低频能量,因此用于助听器的有效风噪声降低算法可能不适用于人工耳蜗。

目的

本研究的目的是调查麦克风方向性对人工耳蜗使用者在风噪声环境下语音识别和感知音质的影响,并得出适用于人工耳蜗的有效风噪声降低策略。

研究设计

采用重复测量设计,以检验通过定向和全向麦克风记录的风噪声所产生的频谱和时间掩蔽效应,以及预加重滤波器对人工耳蜗性能的影响。将一台数字助听器设置为在定向和全向模式下具有线性放大和相对平坦的原位频率响应。然后在安静的风洞中,以4.5米/秒和13.5米/秒的流速,从0到360°记录助听器的输出。

研究样本

16名语后聋的成年人工耳蜗使用者参与了本研究,他们报告称能够在不使用短信的情况下与朋友和家人通过电话交流。

干预

人工耳蜗使用者听取在定向和全向麦克风产生最低噪声水平的位置所记录的风噪声中的语音。

数据收集与分析

人工耳蜗使用者重复句子并对测试材料的音质进行评分。分析了气流噪声的频谱和时间特性,以及预加重滤波器前后的语音和/或噪声特性。还计算了预加重滤波前后语音识别分数与风噪声波峰因数之间的相关系数。

结果

在风速为13.5米/秒时,使用者使用全向麦克风模式比定向麦克风模式获得更高的分数,但在风速为4.5米/秒时,两种麦克风模式下他们获得的语音识别分数相似。预加重滤波后语音识别分数与风噪声波峰因数之间的相关系数高于滤波前。

结论

人工耳蜗使用者将从定向和全向麦克风中受益,以减少远场背景噪声和近场风噪声。如果在预加重滤波器之后分析输入信号以做出麦克风切换决策,自动麦克风切换算法可能会更有效。

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