In this paper, we present an improved algorithm to decompose the harmonic and the noise components of voiced speech. The improvements make the method more accurate and robust by employing a harmonic extrapolation and a noise extrapolation in alternating iterative steps and by including a new pitch detection algorithm. This new technique has been found to improve both the convergence and accuracy of separation of the harmonic and the noise components. In separating the noise and the harmonic components, this improved harmonic-plus-noise (H+N) decomposition method provides many useful ways to measure the strength of voicing. Two such measures are investigated with respect to their ability to discern voiced and unvoiced segments of speech. They are the harmonic-to-noise energy ratio and the sub-band harmonic-to-noise energy ratio. Tests show that these measures perform more reliably and more robustly in comparison to classical measures such as the zero-crossing rate, the LPC prediction gain, the 1/sup st/ LP coefficient and the RMS energy.
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机译：在本文中，我们提出了一种改进的算法来分解浊音的谐波和噪声成分。通过在交替的迭代步骤中采用谐波外推和噪声外推并包括新的音高检测算法，这些改进使该方法更加准确和健壮。已经发现这种新技术可以改善谐波和噪声分量分离的收敛性和准确性。在分离噪声和谐波分量时，这种改进的谐波加噪声（H + N）分解方法提供了许多有用的方法来测量发声强度。就其辨别语音中有声和无声段的能力，研究了两种这样的措施。它们是谐波噪声能量比和子带谐波噪声能量比。测试表明，与诸如过零率，LPC预测增益，1 / sup st / LP系数和RMS能量之类的经典措施相比，这些措施的性能更可靠，更可靠。
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