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Proceedings ArticleDOI

Design of multichannel wiener filter for speech enhancement in hearing aids and noise reduction technique

01 Nov 2016-pp 1-4
TL;DR: Application of wiener algorithm in HA device provides improved speech quality with less complexity and enhanced speech signal can be observed at the output which is available for listener who uses HA device.
Abstract: This paper proposes an algorithm for design of multichannel wiener filter for the application of hearing aids (HA). Present hearing aid devices amplify the speech signal which is corrupted by disturbances and noise from the same environment, resulting degraded speech quality and less efficiency of such devices. Application of wiener algorithm in HA device provides improved speech quality with less complexity. Noticeably enhanced speech quality can be obtained if multiple wiener filter channels are used. Multichannel wiener filter algorithm for speech considers scalar combination of noise inputs to filter and speech correlations. A single target speech system having multiple noise inputs to the filter is designed for estimation of degrading signal or noise. It allows extraction of pure speech by nullifying the estimated noise from corrupted speech of the pilot channel. Enhanced speech signal can be observed at the output which is available for listener who uses HA device. Filter coefficients are extracted from input noise and corrupted speech correlation matrices.
Citations
More filters
Journal ArticleDOI
TL;DR: A state-of-the-art summary and present approaches for using the widely used machine learning and deep learning methods to detect the challenges along with future research directions of speech enhancement systems are provided.
Abstract: Speech enhancement has substantial interest in the utilization of speaker identification, video-conference, speech transmission through communication channels, speech-based biometric system, mobile phones, hearing aids, microphones, voice conversion etc. Pattern mining methods have a vital step in the growth of speech enhancement schemes. To design a successful speech enhancement system consideration to the background noise processing is needed. A substantial number of methods from traditional techniques and machine learning have been utilized to process and remove the additive noise from a speech signal. With the advancement of machine learning and deep learning, classification of speech has become more significant. Methods of speech enhancement consist of different stages, such as feature extraction of the input speech signal, feature selection, feature selection followed by classification. Deep learning techniques are also an emerging field in the classification domain, which is discussed in this review. The intention of this paper is to provide a state-of-the-art summary and present approaches for using the widely used machine learning and deep learning methods to detect the challenges along with future research directions of speech enhancement systems.

33 citations

Posted Content
TL;DR: A novel approach to the cocktail party problem that relies on a fully convolutional neural network architecture, which is able to generalize to new speakers and robustness to new noise environments of varying signal-to-noise ratios is described.
Abstract: This paper will describe a novel approach to the cocktail party problem that relies on a fully convolutional neural network (FCN) architecture. The FCN takes noisy audio data as input and performs nonlinear, filtering operations to produce clean audio data of the target speech at the output. Our method learns a model for one specific speaker, and is then able to extract that speakers voice from babble background noise. Results from experimentation indicate the ability to generalize to new speakers and robustness to new noise environments of varying signal-to-noise ratios. A potential application of this method would be for use in hearing aids. A pre-trained model could be quickly fine tuned for an individuals family members and close friends, and deployed onto a hearing aid to assist listeners in noisy environments.

7 citations


Cites methods from "Design of multichannel wiener filte..."

  • ...Traditional monaural algorithms include spectral subtraction and Wiener filtering [8] - [9] ....

    [...]

Journal ArticleDOI
TL;DR: The evaluation results demonstrated the outperformance of the proposed method compared to the probability density functions of Rayleigh and Gamma distributions in terms of segmental signal-to-noise ratio (segSNR), general SNR, and perceptual evaluation of speech quality (PESQ).
Abstract: A novel single-channel technique was proposed based on a minimum mean square error (MMSE) estimator to enhance short-time spectral amplitude (STSA) in the Discrete Fourier Transform (DFT) domain. In the present contribution, a Weibull distribution was used to model DFT magnitudes of clean speech signals under the additive Gaussian noise assumption. Moreover, the speech enhancement procedure was conducted with (WSPU) and without speech presence uncertainty (WoSPU). The theoretical spectral gain function was obtained as a weighted geometric mean of hypothetical gains associated with signal presence and absence. Extensive experiments were conducted with clean speech signals taken from the TIMIT database, which had been degraded by various additive non-stationary noise sources, and then enhanced signals were evaluated. The evaluation results demonstrated the outperformance of the proposed method compared to the probability density functions (PDF) of Rayleigh and Gamma distributions in terms of segmental signal-to-noise ratio (segSNR), general SNR, and perceptual evaluation of speech quality (PESQ). The performance in the WSPU case was also significantly improved compared to WoSPU, assuming Weibull speech priors in the MMSE-STSA based speech enhancement algorithm.

4 citations

Journal ArticleDOI
TL;DR: In this paper, a voice activity detection technique is designed using features such as short-term energy, periodicity and spectral flatness, and the desired results are obtained by using these three features, even at low signal to noise ratio values.
Abstract: In this study, a voice activity detection technique is designed using features such as short-term energy, periodicity and spectral flatness. The desired results are obtained by using these three features, even at low signal to noise ratio values. In addition, performance of multi-channel noise reduction algorithms such as Wiener speech distortion weighted, spatial prediction, minimum variance distortion-less response are compared using the proposed voice activity detection. Two different audio signals and three different noise types are used in the experiment. Noisy speech and only detection of noisy areas have been performed by proposed voice activity detection algorithm. The filter coefficients have been calculated for each filter algorithm used after detection of noisy speech and only noisy areas. The calculated filter coefficients have been multiplied by the frequency components of the signal received from the reference microphone to obtain an enhanced signal. Segmental signal to noise ratio, an objective method, and mean opinion score as a subjective method have been used to evaluate the performance of the filters. Speech distortion weighted Wiener filter has been found to be the best filter for noise reduction performance.

4 citations

TL;DR: In this article , the authors proposed various speech enhancement algorithms such as Singular Value Decomposition (SVD), log minimum mean square error (log-MMSE) and Wiener.
Abstract: In recent years, Automatic Speech Recognition (ASR) services have performed notable progress in the research efforts of big companies such as Google and Amazon. However, the ASRs are still sensitive to the audio processing quality in other languages. To solve this issue, various speech enhancement algorithms that are the most prominent in improving speech intelligibility were proposed, such as Singular Value Decomposition (SVD), log Minimum Mean Square Error (log-MMSE) and Wiener. By preprocessing the audio files with these algorithms, we seek to reduce the Word Error Rate (WER), which compares the transcription performed by the ASR against a manual transcription. Thus, we can determine the percentage of error that the ASR service has acquired. Results demonstrated that Google is more efficient than Amazon and Vosk counterparts. Also, we decided that applying a Low-pass filter combined with a log-MMSE algorithm to the audio files can substantially reduce the WER percentage of transcription depending on the noise characteristics contained in the audio.
References
More filters
Journal ArticleDOI
TL;DR: This paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction and shows that in the single-channel case the a posteriori signal-to-noise ratio (SNR) is greater than or equal to the a priori SNR (defined before theWiener filter), indicating that the Wieners filter is always able to achieve noise reduction.
Abstract: The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion

563 citations


"Design of multichannel wiener filte..." refers methods in this paper

  • ...Wiener filtering approach has been also used in applications like car environment for noise cancellation [8]....

    [...]

Journal ArticleDOI
TL;DR: This work formally shows that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction, and proposes new simplified expressions for thePMWF, the MVDR, and the generalized sidelobe canceller that depend on the signals' statistics only.
Abstract: Several contributions have been made so far to develop optimal multichannel linear filtering approaches and show their ability to reduce the acoustic noise. However, there has not been a clear unifying theoretical analysis of their performance in terms of both noise reduction and speech distortion. To fill this gap, we analyze the frequency-domain (non-causal) multichannel linear filtering for noise reduction in this paper. For completeness, we consider the noise reduction constrained optimization problem that leads to the parameterized multichannel non-causal Wiener filter (PMWF). Our contribution is fivefold. First, we formally show that the minimum variance distortionless response (MVDR) filter is a particular case of the PMWF by properly formulating the constrained optimization problem of noise reduction. Second, we propose new simplified expressions for the PMWF, the MVDR, and the generalized sidelobe canceller (GSC) that depend on the signals' statistics only. In contrast to earlier works, these expressions are explicitly independent of the channel transfer function ratios. Third, we quantify the theoretical gains and losses in terms of speech distortion and noise reduction when using the PWMF by establishing new simplified closed-form expressions for three performance measures, namely, the signal distortion index, the noise reduction factor (originally proposed in the paper titled ldquoNew insights into the noise reduction Wiener filter,rdquo by J. Chen (IEEE Transactions on Audio, Speech, and Language Processing, Vol. 15, no. 4, pp. 1218-1234, Jul. 2006) to analyze the single channel time-domain Wiener filter), and the output signal-to-noise ratio (SNR). Fourth, we analyze the effects of coherent and incoherent noise in addition to the benefits of utilizing multiple microphones. Fifth, we propose a new proof for the a posteriori SNR improvement achieved by the PMWF. Finally, we provide some simulations results to corroborate the findings of this work.

317 citations


"Design of multichannel wiener filte..." refers methods in this paper

  • ...To fulfill requirement of better noise reduction and less speech distortion, multiple microphones technique is applies in HA device [5-6]....

    [...]

Journal ArticleDOI
TL;DR: It is shown that the SP-SDW-MWF is more robust against signal model errors than the GSC, and that the block-structured step size matrix gives rise to a faster convergence and a better tracking performance than the diagonal step size Matrix, only at a slightly higher computational cost.

167 citations


"Design of multichannel wiener filte..." refers background in this paper

  • ...HA devices are equipped with microphone and signal processing hardware along with speaker [1-3]....

    [...]

Proceedings ArticleDOI
21 Apr 1997
TL;DR: A multichannel-algorithm for speech enhancement for hands-free telephone systems in cars that yields better results in noise reduction with significantly less distortions and artificial noise than spectral subtraction or Wiener filtering alone.
Abstract: This paper presents a multichannel-algorithm for speech enhancement for hands-free telephone systems in cars. This new algorithm takes advantage of the special noise characteristics in fast driving cars. The incoherence of the noise allows to use adaptive Wiener filtering in the frequencies above a theoretically determined frequency. Below this frequency a smoothed spectral subtraction (SSS) is used to get an improved noise suppression. The algorithm yields better results in noise reduction with significantly less distortions and artificial noise than spectral subtraction or Wiener filtering alone.

156 citations


"Design of multichannel wiener filte..." refers methods in this paper

  • ...For this estimation, it makes use of correlation of corrupted speech in pilot channel along with noise in secondary microphones [9]....

    [...]

Journal ArticleDOI
TL;DR: A theoretical study and experimental validation on a binaural hearing aid setup of this standard SDW-MWF implementation, where the effect of estimation errors in the second-order statistics is analyzed and two recently introduced alternative filters are studied.
Abstract: The speech distortion weighted multichannel Wiener filter (SDW-MWF) is a promising multi-microphone noise reduction technique, in particular for hearing aid applications. Its benefit over other single- and multi-microphone techniques has been shown in several previous contributions, theoretically as well as experimentally. In theoretical studies, it is usually assumed that there is a single target speech source. The filter can then be decomposed into a conceptually interesting structure, i.e., into a spatial filter (related to other known techniques) and a single-channel postfilter, which then also allows for a performance analysis. Unfortunately, it is not straightforward to make a robust practical implementation based on this decomposition. Instead, a general SDW-MWF implementation, which only requires a (relatively easy) estimation of speech and noise correlation matrices, is mostly used in practice. This paper features a theoretical study and experimental validation on a binaural hearing aid setup of this standard SDW-MWF implementation, where the effect of estimation errors in the second-order statistics is analyzed. In this case, and for a single target speech source, the standard SDW-MWF implementation is found not to behave as predicted theoretically. Second, two recently introduced alternative filters, namely the rank-one SDW-MWF and the spatial prediction SDW-MWF, are also studied in the presence of estimation errors in the second-order statistics. These filters implicitly assume a single target speech source, but still only rely on the speech and noise correlation matrices. It is proven theoretically and illustrated through experiments that these alternative SDW-MWF implementations behave close to the theoretical optimum, and hence outperform the standard SDW-MWF implementation.

91 citations


"Design of multichannel wiener filte..." refers background or methods in this paper

  • ...Being different from other noise reduction methods, wiener filter calculates noise and speech correlation for estimating noise and it is easy to implement [1]....

    [...]

  • ...In some cases, linearly constrained minimum variance beamformer used which concentrates on direction of speech for more gain [1]....

    [...]

  • ...HA devices are equipped with microphone and signal processing hardware along with speaker [1-3]....

    [...]

  • ...Scalar relation between noise and wiener filter exists [1]....

    [...]