## 3 Modification of MCRA Algorithm
### Speech Presence Probability Adapted by Harmonic Properties  
In (7), the threshold of speech-presence \(\delta_{\gamma}\) is a constant in the MCRA algorithm [3]. If the value of \(\delta_{\gamma}\) is too high, a greater quantity of weak speech spectra, such as weak vowels and consonants, would be classified as noise. The value of the speech indicator function \(I(\eta,\Omega)\) is set to zero. Although the quantity of the residual noise is reduced, speech distortion increases in denoised speech. The quality of denoised speech deteriorates. Conversely, if the value of \(\delta_{\gamma}\) is too small, a greater quantity of noise spectra would be classified as speech. The value of the speech indicator function \(I(\eta,\Omega)\) is falsely set to unity. Although the speech distortion is reduced, the quantity of residual noise increases. Therefore, the denoised speech sounds annoying and uncomfortable. Figure 1: Contour of estimated minimum power. (**a**) Minimum power estimated by the MCRA noise estimator for a sub-band (solid: minimum power, green dotted: true noise power, blue dotted: temporary power, dash dot: local power), spoken by a female speaker, interfered by white noise with an average SegSNR = 10 dB); (**b**) Minimum power estimated by the proposed noise estimator. The harmonic property of vowels is employed to adapt the threshold of speech presence \(\delta_{\gamma}\), given as  
\[\delta_{r}(\eta)=\left\{ \begin{array}{ll}\delta_{V}&\text{, if }F^{\circ}(\eta)=1\\ \delta_{Neighbor}&\text{, if }\sum\limits_{t=-\varepsilon}^{\varepsilon}F^{ \circ}(\eta+t)>0\\ \delta_{N}&\text{, otherwise}\end{array} \right. \tag{17}\]  
where \(\delta_{V}\), \(\delta_{Neighbor}\), and \(\delta_{N}\) represent the thresholds of speech presence for a vowel, the neighbor frames of a vowel, and noise-dominant regions, respectively. The values of the threshold are empirically chosen to be 1.5, 1, and 5, respectively. In (17), the values \(\delta_{V}\), \(\delta_{Neighbor}\), and \(\delta_{N}\) are determined in white noise corruption with various input SNRs at which the selected values can obtain the largest improvement of the average segmental SNR. In the cases of a vowel and the corresponding neighbor frames, the thresholds are small. This prevents weak vowels and consonants from being classified as noise, and then removed by the process of speech denoising. Accordingly, the quality of denoised speech is improved by using the harmonic properties of vowels to adapt the threshold of speech presence. Figure 2 presents the comparisons of estimated probability of speech-presence. The values of the speech-presence probability in the proposed method (Figure 2c) are higher than that of the MCRA method shown in Figure 2b during the offset and onset of vowels. Moreover, the consonants of an utterance also can have the value of the speech-presence probability approaching unity. This is attributed to the thresholds of speech presence being reduced for a vowel and its neighbor frames as given in (17), enabling the speech spectra to be preserved by the process of speech denoising. The quality of denoised speech using the MCRA noise estimator improves. Conversely, the threshold is set to a high level \(\delta_{N}\) during noise-dominant regions, enabling interference noise to be accurately classified as noise, i.e., the corresponding values of speech presence probability approaching zero, in particular at the beginning and ending of the utterance shown in Figure 2c. Figure 2: Contours of estimated speech-presence probability. (**a**) Local power of a sub-band (spoken by a female speaker, interfered by white noise with an average SegSNR = 10 dB); (**b**) speech presence probability estimated by the MCRA estimator; (**c**) speech presence probability estimated by the proposed noise estimator. Figure 3 shows an example of the magnitude contour of the estimated noise power spectrum. Noise estimates are updated during the speech presence and speech-absence periods for the MCRA and proposed methods.