利用经子带能量转换改进的特征识别咳嗽

Chunmei Zhu, Lianfang Tian, Xiangyang Li, Hongqiang Mo, Zeguang Zheng
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引用次数: 9

摘要

本文旨在改进用于咳嗽识别的梅尔频率倒频谱系数(MFCC)。为了突出咳嗽声最显著的特征--高能量,我们提出了一种子带能量转换方法来改进传统的 MFCC。该方法根据对各种咳嗽声的调查所获得的子带能量分布,增强高能量的频带,忽略低能量的频带。使用隐马尔可夫模型(HMM)进行的咳嗽识别实验表明,该方法可将平均识别率从 87% 提高到 91%,并提高了系统在噪声环境中的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognition of cough using features improved by sub-band energy transformation
The purpose of this paper is to improve mel frequency cepstrum coefficients (MFCCs) for cough recognition. To highlight high energy, the most remarkable characteristic of cough sound, we propose a method of sub-band energy transformation to improve traditional MFCCs. This method enhances bands with high energy and ignores the ones with low energy according to the sub-band energy distribution acquired by investigation of varieties of cough sounds. Cough recognition experiments using hidden Markov models (HMMs) show that the average recognition rate rises from 87% to 91% and robustness of the system in noisy environment is improved by the proposed method.
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