## 4 Experimental Results
### Spectrograms  
The quantity of residual noise in denoised speech cannot be easily qualified by an objective measure. To analyze the time-frequency structures of denoised speech and residual noise is particularly important. Observing speech spectrograms can yield more information about the speech distortion and  
Figure 5: Example of a speech signal spoken in Mandarin Chinese by a male speaker. (From top to bottom) (**a**) clean speech; (**b**) speech interfered by helicopter noise with an average SegSNR = 5 dB; (**c**) denoised speech using the MCRA noise estimator; (**d**) denoised speech using the forward-backward MCRA noise estimator; (**e**) denoised speech using the proposed noise estimator.  
Figure 6: Example of a speech signal spoken in Mandarin Chinese by a female speaker. (From top to bottom) (**a**) clean speech; (**b**) speech interfered by factory noise with an average SegSNR = 5 dB; (**c**) denoised speech using the MCRA noise estimator; (**d**) denoised speech using the forward-backward MCRA noise estimator; (**e**) denoised speech using the proposed noise estimator.  
residual noise. Figures 7 and 8 present spectrogram comparisons for denoised speech using various noise estimators.  
Figure 7: Spectrograms of speech spoken by a female speaker, (**a**) clean speech; (**b**) speech interfered by helicopter-cockpit noise with average SegSNR = 10 dB; (**c**) denoised speech using the MCRA noise estimator; (**d**) denoised speech using the forward-backward MCRA noise estimator; (**e**) denoised speech using the proposed noise estimator.  
by speech denoising. Employing the proposed approach is better able to preserve weak vowels and speech components in denoised speech during speech presence regions (marked in ellipse). So the harmonic structure of a vowel by using the proposed approach is better than the other two methods. The quality of denoised speech improves. This is attributed to the increase in the value of the speech presence probability for weak vowels and consonants, yielding the level of noise spectra being underestimated. The quantity of noisy speech that had been suppressed by speech denoising is decreased. Speech distortion is reduced, resulting in denoised speech sounding more comfortable than the other two approaches.  
In Figure 8, a speech signal is heavily corrupted by white noise signals with _Avg_SegSNR = 0_ dB (Figure 8b). By comparing Figure 8c-e, interference noise can be effectively removed by speech denoising. This ensures that an MCRA-based method can be employed to cope with heavy noise corruptions. Although employing the FB_MCRA method can enable interference noise to be significantly removed by speech denoising, the harmonic structure is the worst among the three methods. It causes larger speech distortion than that using the MCRA and proposed noise estimators. The quality of denoised speech deteriorates. On the contrary, the proposed approach can well preserve weak vowels in denoised speech during speech presence regions (marked by ellipse). Therefore, the harmonic structure of a vowel by using the proposed method is better than the other two approaches. The quality of denoised speech is improved. These results confirm that the proposed approach can well estimate the level of noise spectra, even with environments of heavy noise corruption.