## 1 Introduction  
Interference noise deteriorates speech quality and intelligibility. The process of speech denoising can remove the interference noise, so speech denoising is important for the applications of mobile speech communication and multimedia signal processing. The accuracy of noise estimation affects the performance of speech denoising significantly. How to derive an approach to detecting non-stationary noise accurately is important to speech denoising. Many studies have been conducted to estimate noise [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. [PERSON] and [PERSON] [1] proposed a noise estimator, which employed speech presence probability to update noise variance. [PERSON] et al. [2] proposed incorporating spectro-temporal correlations to improve the performance for noise tracking. A minima-controlled-recursive-averaging (MCRA) algorithm is a successful noise estimation approach for speech denoising [3; 4]. The MCRA algorithm estimates noise power by averaging the past spectral power values. The noise power updated according to the probability of speech presence for each sub-band. Many novel methods have been proposed to improve the performance of the MCRA methods [5; 6; 7; 8]. [PERSON] et al. [5] proposed a method to shorten time delay for the detection of abrupt changes in noise. Noise update criteria were also additionally controlled to reduce speech leakage for the MCRA algorithm. [PERSON] and [PERSON] [6] proposed conditional maximizing a posteriori criterion with a second order to improve the performance of the MCRA algorithm. [PERSON] et al. [7] proposed a modified version of the time variant recursive averaging of the MCRA algorithm by utilizing both noise and speech segments. In addition, speech denoising residue was employed to approximate the noise signal and to update noise spectra in speech-activity regions. Based on the above discussions, most of the noise estimation methods do not consider speech properties in noise estimation. In this study, we employed the harmonic properties of a vowel to determine the segment length for tracking minimum statistics in the MCRA algorithm. In the case of a vowel frame and its neighbors, we perform the increment of segment length and the decrement of threshold for speech presence. This enables the MCRA algorithm to pick up the lower magnitude as a noise level. The noise estimate tends to be underestimated. This yields speech distortion reduction in the denoised signal. The quality of denoised speech is then improved. Conversely, the segment length decreases during noise-dominant frames. This enables the MCRA algorithm to update the level of noise estimate quickly and track noise variation accurately. The process of speech denoising can remove interference noise more effectively. Accordingly, denoised speech by using the proposed noise estimator sounds more comfortable than that using the MCRA algorithm. The noise estimation performance of the MCRA algorithm is therefore improved. In [4], an improved MCRA algorithm was proposed. This method estimates noise by averaging past spectral power values. The smoothing factor is adapted by the speech-presence probability controlled by the minima values of a smoothed periodogram. This method comprises two iterations for smoothing and minimum tracking. In [11], the noise estimate is updated by averaging the noisy speech power spectrum using smoothing factors adapted by the speech-presence probability, which is determined by the ratio of the noisy speech power spectrum to its local minimum. The differences between the proposed method and the other two methods [4; 12] are that the proposed method considers the harmonic properties to control the segment length, which is utilized for updating the minimum power. This minimum power is employed to determine the value of signal-presence probability. In addition, the threshold of speech-presence probability is also determined according to the class of noisy speech, including vowel frames, neighbor frames of a vowel, and noise-dominant frames.