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Cough Sound Discrimination in Noisy Environments using Microphone Array

机译:使用麦克风阵列在嘈杂环境中咳出声音歧视

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Cough sound discriminator algorithms are capable of distinguishing between dry and wet cough types. The performance of such algorithms, however, is affected by noise and reverberation in the environment. The effect of reverberation on the performance of cough sound discriminators was previously studied in [1]. In this paper, the effect of noise on the performance of cough sound discriminator is studied and quantitatively measured using previously defined Linear Separation Score (LSS) [1]. Experiments revealed a significant decrease in the performance of cough sound discriminator in the presence of white noise using a single microphone for cough sound acquisition. A microphone array structure containing a maximum of 7 microphones along with delay-and-sum beamforming algorithm was used to improve the performance of the cough sound discriminator. Experimental results showed improvement in the performance of the cough sound discriminator in the presence of white noise using microphone arrays.
机译:咳嗽声鉴别器算法能够区分干燥和湿咳嗽类型。然而,这种算法的性能受到环境中的噪声和混响的影响。 [1]中,先前研究了混响对咳嗽声鉴别器性能的影响。在本文中,使用先前定义的线性分离得分(LSS)研究和定量测量噪声对咳嗽声鉴别器性能的影响[1]。实验显示使用单个麦克风在白噪声存在下进行咳嗽声判别器的性能显着降低,用于咳嗽声获取。使用最多7个麦克风的麦克风阵列结构以及延迟和和波束形成算法用于提高咳嗽声鉴别器的性能。实验结果表明使用麦克风阵列存在白噪声在白噪声中的性能的提高。

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