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Noise reduction using wavelet thresholding of multitaper estimators and geometric approach to spectral subtraction for speech coding strategy.

Chu KC, Choi CT - Clin Exp Otorhinolaryngol (2012)

Bottom Line: Noise reduction using wavelet thresholding of multitaper estimators (WTME) and geometric approach to spectral subtraction (GASS) can improve speech quality of noisy sound for speech coding strategy.This study included 25 Mandarin sentences as test materials.There is no significant difference between the overall performance of sound quality in both methods, but the geometric approach to spectral subtraction method is slightly better than the wavelet thresholding of multitaper estimators.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Institute of Biomedical Engineering, National Chiao Tung University, Taiwan.

ABSTRACT

Objectives: Noise reduction using wavelet thresholding of multitaper estimators (WTME) and geometric approach to spectral subtraction (GASS) can improve speech quality of noisy sound for speech coding strategy. This study used Perceptual Evaluation of Speech Quality (PESQ) to assess the performance of the WTME and GASS for speech coding strategy.

Methods: This study included 25 Mandarin sentences as test materials. Environmental noises including the air-conditioner, cafeteria and multi-talker were artificially added to test materials at signal to noise ratio (SNR) of -5, 0, 5, and 10 dB. HiRes 120 vocoder WTME and GASS noise reduction process were used in this study to generate sound outputs. The sound outputs were measured by the PESQ to evaluate sound quality.

Results: Two figures and three tables were used to assess the speech quality of the sound output of the WTME and GASS.

Conclusion: There is no significant difference between the overall performance of sound quality in both methods, but the geometric approach to spectral subtraction method is slightly better than the wavelet thresholding of multitaper estimators.

No MeSH data available.


The overall mean value of each signal to noise ratio (SNR).
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Figure 5: The overall mean value of each signal to noise ratio (SNR).

Mentions: The performance of the two methods in measuring speech quality is showed in Tables 1-3 and Figs. 4, 5. Firstly, Table 1 showed the PESQ scores of 25 sentences from difference background environments (air-conditioner, cafeteria and multi-talker) at SNRs of -5, 0, 5, and 10 dB that processed by the WTME noise reduction method in the HiRes 120 vocoder. The PESQ scores of 25 sentences from difference background environments (air-conditioner, cafeteria and multi-talker) at SNRs of -5, 0, 5, and 10 dB that processed by the GASS noise reduction method in the HiRes 120 vocoder is showed in Table 2. Table 3 showed the PESQ mean scores of sentences from difference background environments (air-conditioner, cafeteria and multi-talker) at difference SNR that processed by the WTME and GASS noise reduction methods in the HiRes 120 vocoder. Fig. 4 shows the mean value of each SNR based on difference background environment whereas Fig. 5 shows the overall PESQ mean value of each SNR.


Noise reduction using wavelet thresholding of multitaper estimators and geometric approach to spectral subtraction for speech coding strategy.

Chu KC, Choi CT - Clin Exp Otorhinolaryngol (2012)

The overall mean value of each signal to noise ratio (SNR).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369986&req=5

Figure 5: The overall mean value of each signal to noise ratio (SNR).
Mentions: The performance of the two methods in measuring speech quality is showed in Tables 1-3 and Figs. 4, 5. Firstly, Table 1 showed the PESQ scores of 25 sentences from difference background environments (air-conditioner, cafeteria and multi-talker) at SNRs of -5, 0, 5, and 10 dB that processed by the WTME noise reduction method in the HiRes 120 vocoder. The PESQ scores of 25 sentences from difference background environments (air-conditioner, cafeteria and multi-talker) at SNRs of -5, 0, 5, and 10 dB that processed by the GASS noise reduction method in the HiRes 120 vocoder is showed in Table 2. Table 3 showed the PESQ mean scores of sentences from difference background environments (air-conditioner, cafeteria and multi-talker) at difference SNR that processed by the WTME and GASS noise reduction methods in the HiRes 120 vocoder. Fig. 4 shows the mean value of each SNR based on difference background environment whereas Fig. 5 shows the overall PESQ mean value of each SNR.

Bottom Line: Noise reduction using wavelet thresholding of multitaper estimators (WTME) and geometric approach to spectral subtraction (GASS) can improve speech quality of noisy sound for speech coding strategy.This study included 25 Mandarin sentences as test materials.There is no significant difference between the overall performance of sound quality in both methods, but the geometric approach to spectral subtraction method is slightly better than the wavelet thresholding of multitaper estimators.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Institute of Biomedical Engineering, National Chiao Tung University, Taiwan.

ABSTRACT

Objectives: Noise reduction using wavelet thresholding of multitaper estimators (WTME) and geometric approach to spectral subtraction (GASS) can improve speech quality of noisy sound for speech coding strategy. This study used Perceptual Evaluation of Speech Quality (PESQ) to assess the performance of the WTME and GASS for speech coding strategy.

Methods: This study included 25 Mandarin sentences as test materials. Environmental noises including the air-conditioner, cafeteria and multi-talker were artificially added to test materials at signal to noise ratio (SNR) of -5, 0, 5, and 10 dB. HiRes 120 vocoder WTME and GASS noise reduction process were used in this study to generate sound outputs. The sound outputs were measured by the PESQ to evaluate sound quality.

Results: Two figures and three tables were used to assess the speech quality of the sound output of the WTME and GASS.

Conclusion: There is no significant difference between the overall performance of sound quality in both methods, but the geometric approach to spectral subtraction method is slightly better than the wavelet thresholding of multitaper estimators.

No MeSH data available.