Noise Estimation for Speech Enhancement Using Minimum-Spectral-Average and Vowel-Presence Detection Approach

Chen, Yung Yue; Shen, Jui-Lung; Wang, Ling Ling; Lei, Chung Lin

doi:10.1007/978-981-10-3187-8_32

Cited by 1 publication

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“…Unfortunately, these methods usually work for slowly-changing noise. Other methods exploit the fact that noise usually does not change very rapidly, so noise is estimated by averaging it from previous frames [39][40][41][42]. The methods apply the adaptation speed weight factor to control how much the current frames affect the noise estimate.…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement for Secure Communication Using Coupled Spectral Subtraction and Wiener Filter

et al. 2019

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The encryption process for secure voice communication may degrade the speech quality when it is applied to the speech signals before encoding them through a conventional communication system such as GSM or radio trunking. This is because the encryption process usually includes a randomization of the speech signals, and hence, when the speech is decrypted, it may perceptibly be distorted, so satisfactory speech quality for communication is not achieved. To deal with this, we could apply a speech enhancement method to improve the quality of decrypted speech. However, many speech enhancement methods work by assuming noise is present all the time, so the voice activity detector (VAD) is applied to detect the non-speech period to update the noise estimate. Unfortunately, this assumption is not valid for the decrypted speech. Since the encryption process is applied only when speech is detected, distortions from the secure communication system are characteristically different. They exist when speech is present. Therefore, a noise estimator that is able to update noise even when speech is present is needed. However, most noise estimator techniques only adapt to slow changes of noise to avoid over-estimation of noise, making them unsuitable for this task. In this paper, we propose a speech enhancement technique to improve the quality of speech from secure communication. We use a combination of the Wiener filter and spectral subtraction for the noise estimator, so our method is better at tracking fast changes of noise without over-estimating them. Our experimental results on various communication channels indicate that our method is better than other popular noise estimators and speech enhancement methods.

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Section: Introductionmentioning

confidence: 99%