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一种基于倒谱的语音信号谐波噪声比测定技术。

A cepstrum-based technique for determining a harmonics-to-noise ratio in speech signals.

作者信息

de Krom G

机构信息

Research Institute for Language and Speech, University of Utrecht, The Netherlands.

出版信息

J Speech Hear Res. 1993 Apr;36(2):254-66. doi: 10.1044/jshr.3602.254.

DOI:10.1044/jshr.3602.254
PMID:8487518
Abstract

A new method to calculate a spectral harmonics-to-noise ratio (HNR) in speech signals is presented. The method involves discrimination between harmonic and noise energy in the magnitude spectrum by means of a comb-liftering operation in the cepstrum domain. Sensitivity of HNR to (a) additive noise and (b) jitter was tested with synthetic vowel-like signals, generated at 10 fundamental frequencies. All jitter and noise signals were analyzed at three window lengths in order to investigate the effect of the length of the analysis frame on the estimated HNR values. Results of a multiple linear regression analysis with noise or jitter, F0, and window length as predictors for HNR indicate a major effect of both noise and jitter on HNR, in that HNR decreases almost linearly with increasing noise levels or increasing jitter. The influence of F0 and window length on HNR is small for the jittered signals, but HNR increases considerably with increasing F0 or window length for the noise signals. We conclude that the method seems to be a valid technique for determining the amount of spectral noise, because it is almost linearly sensitive to both noise and jitter for a large part of the noise or jitter continuum. The strong negative relation between HNR and jitter illustrates that spectral noise measures cannot simply be taken as indicators of the actual amount of noise in the time signal. Instead, HNR integrates several aspects of the acoustic stability of the signal. As such, HNR may be a useful parameter in the analysis of voice quality, although it cannot be directly interpreted in terms of underlying glottal events or perceptual characteristics.

摘要

提出了一种计算语音信号频谱谐波噪声比(HNR)的新方法。该方法通过在倒谱域进行梳状滤波操作,在幅度谱中区分谐波能量和噪声能量。使用在10个基频下生成的类元音合成信号,测试了HNR对(a)加性噪声和(b)抖动的敏感性。为了研究分析帧长度对估计的HNR值的影响,对所有抖动和噪声信号在三个窗口长度下进行了分析。以噪声或抖动、F0和窗口长度作为HNR预测因子的多元线性回归分析结果表明,噪声和抖动对HNR均有主要影响,即HNR几乎随噪声水平或抖动的增加而呈线性下降。对于抖动信号,F0和窗口长度对HNR的影响较小,但对于噪声信号,HNR随F0或窗口长度的增加而显著增加。我们得出结论,该方法似乎是确定频谱噪声量的有效技术,因为在噪声或抖动连续体的很大一部分范围内,它对噪声和抖动几乎呈线性敏感。HNR与抖动之间的强负相关表明,频谱噪声测量不能简单地作为时间信号中实际噪声量的指标。相反,HNR整合了信号声学稳定性的几个方面。因此,HNR可能是语音质量分析中的一个有用参数,尽管它不能直接根据潜在的声门事件或感知特征进行解释。

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