Multi-channel noise reduction for hands-free voice communication on mobile phones

Jin, Wenyu; Taghizadeh, Mohammad Javad; Chen, Kainan; Xiao, Wei

doi:10.1109/icassp.2017.7952207

Cited by 14 publications

(13 citation statements)

References 20 publications

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“…The estimate found in (16) will be used for (8) to find the enhanced speech X(m, k). For the first frame, we assumed D(m − 1, k) to be equal to D(m, k), and they were estimated using the the average spectra of the first five frames of an utterance.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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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: The Proposed Methodsmentioning

confidence: 99%

“…SE is an active area of research that aims to remove noise from speech, and hence, the quality of the speech signals may be improved. This is very crucial in many speech-based applications such as speech recognition [2,3], hearing aid devices [4,5], mobile communications [6], and hands-free devices [7,8].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…This spatial information is used along with a single-channel SPP detector to estimate the noise at the reference channel and the signal-to-noise ratio (SNR), which are needed for the filter design. This proposal was later extended to general multi-channel devices in Reference [19].…”

Section: Introductionmentioning

confidence: 99%

“…P t|t−1 = E a 21,t − a 21,t|t−1 a 21,t − a 21,t|t−1 (19) are error covariance matrices. The Kalman filter is initialized using the overall mean and covariance of the RTF, that is, a 21,0 = µ a 21 and P 0 = Σ a 21 .…”

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confidence: 99%

Dual-Channel Speech Enhancement Based on Extended Kalman Filter Relative Transfer Function Estimation

et al. 2019

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This paper deals with speech enhancement in dual-microphone smartphones using beamforming along with postfiltering techniques. The performance of these algorithms relies on a good estimation of the acoustic channel and speech and noise statistics. In this work we present a speech enhancement system that combines the estimation of the relative transfer function (RTF) between microphones using an extended Kalman filter framework with a novel speech presence probability estimator intended to track the noise statistics' variability. The available dual-channel information is exploited to obtain more reliable estimates of clean speech statistics. Noise reduction is further improved by means of postfiltering techniques that take advantage of the speech presence estimation. Our proposal is evaluated in different reverberant and noisy environments when the smartphone is used in both close-talk and far-talk positions. The experimental results show that our system achieves improvements in terms of noise reduction, low speech distortion and better speech intelligibility compared to other state-of-the-art approaches.

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“…The noise PSD is employed to estimate the gain function to be applied on the reference channel. Such an algorithm was extended for multimicrophone devices in [16]. Nevertheless, all of these techniques make assumptions about the noise field properties that may not be accurate in practice, thereby leading to a limited performance.…”

Section: Introductionmentioning

confidence: 99%

A postfiltering approach for dual-microphone smartphones

Martín-Doñas¹,

López-Espejo²,

Gómez³

et al. 2018

IberSPEECH 2018

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Although beamforming is a powerful tool for microphone array speech enhancement, its performance with small arrays, such as the case of a dual-microphone smartphone, is quite limited. The goal of this paper is to study different postfiltering approaches that allow for further noise reduction. These postfilters are applied to our previously proposed extended Kalman filter framework for relative transfer function estimation in the context of minimum variance distortionless response beamforming. We study two different postfilters based on Wiener filtering and non-linear estimation of the speech amplitude. We also propose several estimators of the clean speech power spectral density which exploit the speaker position with respect to the device. The proposals are evaluated when applying speech enhancement on a dual-microphone smartphone in different noisy acoustic environments, in terms of both perceptual quality and speech intelligibility. Experimental results show that our proposals achieve further noise reduction in comparison with other related approaches from the literature.

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Multi-channel noise reduction for hands-free voice communication on mobile phones

Cited by 14 publications

References 20 publications

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

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

Dual-Channel Speech Enhancement Based on Extended Kalman Filter Relative Transfer Function Estimation

A postfiltering approach for dual-microphone smartphones

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