Speech Enhancement Based onWavelet Thresholding the Multitaper Spectrum

In this paper, we provide a theoretical analysis of the minimum mean-square error short-time spectral amplitude (MMSE-STSA) estimator with the biased a priori SNR estimation and its extension to musical-noise-free speech enhancement. Recently, musical-noise-free speech enhancement has been proposed, where no musical noise is generated in iterative spectral subtraction. However, no existence of the musical-noise-free condition in the MMSE-STSA estimator has been reported. Therefore, in this paper, we show that the musical-noise-free condition exists in the biased MMSE-STSA estimator via the theoretical analysis. In addition, we perform comparative experiments and clarify the efficacy of the proposed musical-noise-free speech enhancement.

Classical speech enhancement algorithms often require a good estimation of the short-time power spectrum using, for instance, the periodogram methods. However, it is well known that traditional periodogram methods are prone to induce large variance, hence produces the “musical noise” after enhancement. To alleviate this problem, multitaper spectrum (MTS) estimators with wavelet denoising were proposed. In this paper, we investigate the properties of the MTS of noisy speech signals. We find that, in the log MTS domain, the variance of noise varies according to the magnitude of the underlying speech spectrum. It implies that when applying wavelet denoising to the log MTS, the constant threshold used in the traditional methods is not appropriate. Based on this observation, we further develop a wavelet denoising method with adaptive threshold for estimating power spectrum using multitaper. Simulation results show that the spectrum estimated using the proposed method is consistently more accurate than the traditional uniform thresholding methods. Hence, it further improves the current speech enhancement algorithms using the MTS approaches.

In this paper, we discuss the amount of musical noise generation for blind speech extraction using minimum mean-square error short-time spectral amplitude (MMSE-STSA) estimator. To achieve high quality speech enhancement, we have previously proposed blind spatial subtraction array (BSSA). However, BSSA always suffers from artificial distortion, so-called musical noise, owing to nonlinear signal processing. Therefore, we propose the improved BSSA using the MMSE-STSA estimator and its generalized method as the post-processing part of BSSA. Also, we conduct a theoretical analysis for the amount of musical noise generation in the proposed method. From the theoretical analysis and objective evaluation results, we derive the optimal parameter for the proposed method.

Two gain forms of spectral amplitude subtraction are derived theoretically without neglecting the correlation of speech and noise spectrum during the period of a frame. In the implementation, the constrained gain is expressed as a function of noncausal a priori SNR (Signal-to-Noise Ratio). Noise and noncausal a priori SNR are estimated from the multitaper spectrum of the noisy signal with algorithms modified to be suitable for the multitaper spectrum. Objective evaluations show that in case of white Gaussian noise the proposed method outperforms some methods based on LSA (Log Spectral Amplitude) in terms of MBSD (Modified Bark Spectral Distortion), segmental SNR and overall SNR, and informal listening tests show that speech reconstructed in this way has little speech distortion and musical noise is nearly inaudible even at low SNR.

As single channel speech enhancement, common methods based on short-time spectral amplitude estimation such as spectral-subtraction and Wiener filtering are considered to be simple and effective for suppressing the additive noise in noisy environments. However, these techniques may distort the high frequency harmonics of speech, which are important to the language understanding. To solve this problem, we propose a new approach. It works by using a comb filter in time domain after Wiener filtering. With the harmonics of voiced speech enhanced and most of the noise suppressed, another comb filter in frequency domain is used to recalculate the suppression gain function, renewing the high frequency harmonics that have been compressed. This new method is theoretically derived, and experimentally demonstrates an improvement for harmonic enhancement and noise reduction.

An effective noise reduction algorithm based on signal analysis in sub-bands is proposed. Speech signal is analyzed in sub-bands using analytic decomposition in hearing critical bands where the envelopes and phases of the analytic signals are used. Noise reduction uses minimum mean square error log-spectral amplitude estimation (MMSE LSAE) noise suppression rule which preserves speech components while producing less musical residual noise. Experiments show that the nature of the band analysis in this work results in less musical noise in comparison with Fourier transform based methods. Moreover noise estimation algorithm in this work is a minima tracking algorithm which significantly reduces the level of musical noise when used in combination with MMSE LSAE.

Reduction of musical residual noise for speech enhancement using masking properties and optimal smoothing

This paper focuses on two representative single-microphone noise reduction problems: speech distortion and musical noise. Many noise reduction methods have been proposed for each problem. Harmonic regeneration noise reduction (HRNR) was introduced for the improvements of speech distortion and the a priori signal-to-noise ratio (SNR) estimator. HRNR involves using a unique signal to regenerate harmonics, which had been eliminated. Musical-noise-free noise reduction based on the minimum-mean square error short-time spectral amplitude estimator (musical-noise-free MMSE-STSA estimator) has also been proposed. This method can suppress a noisy signal without generating musical noise by introducing a bias into the classical a priori SNR estimator. We propose a noise reduction method for addressing these problems simultaneously by improving the classical a priori SNR estimator. We investigated the behavior of the internal parameters for the proposed and conventional methods with regard to speech quality and show the effectiveness of the proposed method in terms of speech distortion and musical noise. We consider and discuss the relationship between the estimation accuracy of an a priori SNR and speech quality. Specifically, we consider the factors to improve speech quality in terms of biasing.

Over the past decades the problem of one channel, speech enhancement has been addressed by a great deal of researchers. In this work selected methods belonging to a variety of categories are applied to denoise speech signals corrupted by non-stationary urban noise. The performance of spectral subtraction, signal subspace, model-based and Kalman filtering approaches is evaluated. Several objective measures which are designed to predict human listening tests are employed in order to reach accurate conclusions. Two series of experiments were carried out while multiband spectral subtraction along with a short-time spectral amplitude (STSA) estimator based on the minimization of the mean square error (MSE) of the log-spectra are shown to outperform the rest of the algorithms.

A speech enhancement system for noisy speech signals under different environments, namely airport, babble, car, exhibition, restaurant, station, street and train, is designed and implemented in this paper. Spectral subtraction is the most popular method for speech enhancement which suffers from perceptually annoying musical noise. Musical noise is reduced here by using low variance spectrum estimators and enhancement is achieved by wavelet thresholding the multitaper spectrum of the speech. The main feature of the proposed system is a novel switching mechanism to an optimal wavelet filter bank or wavelet packet filter bank matching to the critical bands of the human ear, based on the type of noise present in the input speech. The system is evaluated using noisy speech signals under different environment noises at different signal to noise ratios. The subjective and objective test results show that the proposed method improves the quality and intelligibility of speech signals.

Afin de permettre à la Bibliothèque Nationale la restauration en temps réel d'enregistrements sonores dégradés, une structure parallèle basée sur multiprocesseurs Motorola DSP96002 est développée. L'étude porte sur les méthodes de soustraction des bruits par l'estimation du spectre à court terme. La rapidité et la souplesse d'une structure gérée par notre programme pour WINDOWS offrent la possibilité de réaliser sur PC un système adaptatif très performant pour ce type de traitement.

Audio processing methods operating on a time-frequency representation of the signal can introduce unpleasant sounding artifacts known as musical noise. These artifacts are observed in the context of audio coding, speech enhancement, and source separation. The change in kurtosis of the power spectrum introduced during the processing was shown to correlate with the human perception of musical noise in the context of speech enhancement, leading to the proposal of measures based on it. These baseline measures are here shown to correlate with human perception only in a limited manner. As ground truth for the human perception, the results from two listening tests are considered: one involving audio coding and one involving source separation. Simple but effective perceptually motivated improvements are proposed and the resulting new measure is shown to clearly outperform the baselines in terms of correlation with the results of both listening tests. Moreover, with respect to the listening test on musical noise in audio coding, the exhibited correlation is nearly as good as the one exhibited by the Artifact-related Perceptual Score (APS), which was found to be the best objective measure for this task. The APS is however computationally very expensive. The proposed measure is easily computed, requiring only a fraction of the computational cost of the APS.

A new speech enhancement scheme based on higher order cumulants (HOC) is proposed. Under the condition of Gaussian noise, a method for estimating the probability of speech absence in each frequency bin of each frame is devised based on HOC. Further, a kind of modified Minimum Mean Square Error (MMSE) estimator based on HOC is proposed. Objective measurements and subjective tests show that the proposed algorithm could restrain the residual noise and "musical noise" more effectively than the traditional spectral subtraction method and MMSE method particularly under the condition of low signal-to-noise ratio (SNR).

A reliable estimator for speech presence probability (SPP) can significantly improve the performance of many speech enhancement algorithms. Previous work showed that a good SPP estimator can be obtained by using a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. In this paper, a wavelet based denoising algorithm is proposed for such purpose. We first apply the wavelet transform to the periodogram of a noisy speech signal to generate an oracle for indicating the locations of the noise floor in the periodogram. We then make use of that oracle to selectively remove the wavelet coefficients of the noise floor in the log multitaper spectrum (MTS) of the noisy speech. The remaining wavelet coefficients are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables a significantly improvement in the quality and intelligibility of the enhanced speeches. © 2012 IEEE.

This paper describes a method for enhancing speech corrupted by broadband noise. The method is based on the spectral noise subtraction method. The original method entails subtracting an estimate of the noise power spectrum from the speech power spectrum, setting negative differences to zero, recombining the new power spectrum with the original phase, and then reconstructing the time waveform. While this method reduces the broadband noise, it also usually introduces an annoying "musical noise". We have devised a method that eliminates this "musical noise" while further reducing the background noise. The method consists in subtracting an overestimate of the noise power spectrum, and preventing the resultant spectral components from going below a preset minimum level (spectral floor). The method can automatically adapt to a wide range of signal-to-noise ratios, as long as a reasonable estimate of the noise spectrum can be obtained. Extensive listening tests were performed to determine the quality and intelligibility of speech enhanced by our method. Listeners unanimously preferred the quality of the processed speech. Also, for an input signal-to-noise ratio of 5 dB, there was no loss of intelligibility associated with the enhancement technique.