Objective comparison of speech enhancement algorithms under real world conditions

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.

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.

The spectral subtraction method is a conventional approach for single channel speech enhancement. The basic principle of this method is to estimate the short-time spectral magnitude of speech by subtracting estimated noise from the noisy speech spectrum and to combines it with the phase of the noisy speech. Besides reducing the noise, this method generates an unnatural and unpleasant noise, called remnant noise. This paper proposes a novel algorithm to reduce the remnant noise, and thus improving the overall quality of the enhanced speech. In this algorithm, the output of multi-band spectral subtraction (MBSS) method is used as the input signal again for next iteration process. After the MBSS method, the additive noise is changed to remnant noise. The remnant noise is re-estimated at each iteration. The new estimated noise, furthermore, is been used to process the next MBSS. This procedure is iterated a small number of times. The simulation results as well as informal subjective evaluations prove that the speech enhanced by proposed algorithm is more pleasant to listeners than the conventional MBSS algorithm. This reveals that the proposed algorithm reduces remnant noise satisfactorily and produces good speech quality with improved signal-to-noise ratio.

In this paper, we propose a robust speech enhancement algorithm for nonstationary noise environments, which comprises a multiband spectral subtraction (MBSS) speech estimator and a minima controlled recursive averaging (MCRA) noise estimation. A multiband approach is obtained by dividing the whole spectrum into multiple subbands and applying spectral subtraction independently in each band. The noise estimate exploit the observation that the noise signal typically has a nonuniform effect on the spectrum of speech and is given by averaging the past spectrum power value and using a time and frequency dependent smoothing factor that is calculated based on the speech presence probability in subbands. We test the performance of proposed algorithm in various nonstationary noise environments. The experiment results confirm the superiority of the MBSS and MCRA estimator. Outstanding speech enhancement is achieved, while avoiding the residual musical noise phenomena.

The paper describes a new speech enhancement approach which employs adaptive /spl beta/-order minimum mean square error (MMSE) spectral estimation of the short time spectral amplitude (STSA) of a speech signal. In the proposed approach, the human perceptual auditory masking effect is incorporated into the speech enhancement algorithm. The relationship between the value of /spl beta/ and the noise masking threshold is considered. The algorithm is based on a criterion by which the audible noise may be masked rather than being attenuated, thereby reducing the chance of speech distortion. Performance assessment is given to show that our proposal can achieve a more significant noise reduction and a better spectral estimation of weak speech spectral components from a noisy signal as compared to many existing speech enhancement algorithms.

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. The first task in the automatic analysis of snore-related sounds (SRS) is to segment the SRS data as accurately as possible into three main classes: snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. SRS data are generally contaminated with background noise. In this paper, we present classification performance of a new segmentation algorithm based on pattern recognition. We considered four features derived from SRS to classify samples of SRS into three classes. The features—number of zero crossings, energy of the signal, normalized autocorrelation coefficient at 1 ms delay and the first predictor coefficient of linear predictive coding (LPC) analysis—in combination were able to achieve a classification accuracy of 90.74% in classifying a set of test data. We also investigated the performance of the algorithm when three commonly used noise reduction (NR) techniques in speech processing—amplitude spectral subtraction (ASS), power spectral subtraction (PSS) and short time spectral amplitude (STSA) estimation—are used for noise reduction. We found that noise reduction together with a proper choice of features could improve the classification accuracy to 96.78%, making the automated analysis a possibility.

Although electrolarynx (EL) serves as an important method of phonation for the laryngectomees, the resulting speech is of poor intelligibility due to the presence of a steady background noise caused by the instrument, even worse in the case of additive noise. This paper investigates the problem of EL speech enhancement by taking into account the frequency-domain masking properties of the human auditory system. One approach is incorporating an auditory masking threshold (AMT) for parametric adaptation in a subtractive-type enhancement process. The other is the supplementary AMT (SAMT) algorithm, which applies a cross-correlation spectral subtraction (CCSS) approach as a post-processing scheme to enhancing EL speech dealt with the AMT method. The performance of these two algorithms was evaluated as compared to the power spectral subtraction (PSS) algorithm. The best performance of EL speech enhancement was associated with the SAMT algorithm, followed by the AMT algorithm and the PSS algorithm. Acoustic and perceptual analyses indicated that the AMT and SAMT algorithms achieved the better performances of noise reduction and the enhanced EL speech was more pleasant to human listeners as compared to the PSS algorithm.

This paper proposes an algorithm that adopts the harmonic regeneration as post-processing to improve the performance of speech enhancement using traditional Short Time Spectral Amplitude (STSA). The proposed algorithm aims to alleviate the distortion of the high harmonics of enhanced speech via the traditional STSA, and consequently improves the speech quality. We first detect the pitch, or fundamental frequency, of the enhanced speech via the traditional STSA, and then, divide the whole spectrum into multiple sub-bands which center on each harmonic. After that, a series of specially designed windows centered on each harmonic are applied to all the sub-bands, in order to redistribute the energy in the sub-bands. The results of experiment demonstrate that the method has both theoretical and practical basis.

In this paper, we present a new Speech Enhancement (SE) technique capable of running on a smartphone, as an assistive device for hearing aids (HAs). The developed method incorporates the coherence between the speech and noise signals to obtain a SE gain function which is used in conjunction with the gain function obtained by Spectral Subtraction using adaptive gain averaging. SE using coherence based gain function is found to suppress the background noise well, while inducing speech distortion. On the other hand, SE using Spectral Subtraction improves speech quality with tolerable speech distortion, but introduces background musical noise for certain noise types. The weighted fusion of the two gain functions strikes a balance between noise suppression and speech distortion. Also it allows the user to control the weighting factor based on the noisy environment and their comfort level of hearing. The developed method is computationally fast and operates in real-time. The proposed method was evaluated for machinery, babble, and car noise types, using both objective and subjective measures for both quality and intelligibility of the enhanced speech. The results show significant improvements in comparison with stand-alone Spectral Subtraction with weighted gain averaging SE methods.

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.

It is well known that the "musical noise" encountered in most frequency domain speech enhancement algorithms is partially due to the large variance estimates of the spectra. To address this issue, we propose in this paper the use of low-variance spectral estimators based on wavelet thresholding the multitaper spectra for speech enhancement. A short-time spectral amplitude estimator is derived which incorporates the wavelet-thresholded multitaper spectra. Listening tests showed that the use of multitaper spectrum estimation combined with wavelet thresholding suppressed the musical noise and yielded better quality than the subspace and MMSE algorithms.

Speech enhancement is of paramount importance in improving the intelligibility and audibility of speech signals in an uncontrolled noisy environment over wireless communication systems. However, the existing techniques used to achieve this such as spectral subtraction are characterized by remnant noise or musical note, while Kalman filter which can remove the remnant noise is associated with instability and non-linear characteristics. Therefore, this paper proposes speech enhancement model in noisy environment using hybrid spectral-Kalman filter over wireless communication system. The proposed model was carried out using acquired noisy speech signals by the Samsung Android mobile phone. The phone call was made in an uncontrolled noisy environment. The signal received was processed by the hybrid spectral-Kalman filter which was achieved by using the output speech signal from the spectral subtraction in time domain as the input of the Kalman filter. The proposed hybrid spectral-Kalman filtering technique was evaluated using Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI). Comparison was then performed with the existing techniques and validation was performed using corrupted speech signals obtained from the NOIZEUS corpus data set. The result revealed that the proposed technique performed better than the existing techniques in a noisy environment.

This paper presents a formant-tracking linear prediction (FTLP) model for speech processing in noise. The main focus of this work is the detection of formant trajectory based on Hidden Markov Models (HMM), for improved formant estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of a time- sequence of peaks which satisfies continuity constraints on parameter; the within peaks are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning multi-band spectral subtraction stage to reduce the effect of residue noise on formants (2) estimation stage where an initial estimate of the LP model of speech for each frame is obtained (3) a formant classification using probability models of formants and Viterbi-decoders. The evaluation results for the estimation of the formant tracking LP model tested in Gaussian white noise background, demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and LPC variable order analysis, results in a significant reduction in errors and distortions. The performance was evaluated with noisy natual vowels extracted from international french and English vocabulary speech signals at SNR value of 10dB. In each case, the estimated formants are compared to reference formants.

In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two SNRs by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the speech quality along three dimensions: signal distortion, noise distortion and overall quality. This paper reports the correlations of five common objective measures with these three subjective measures. Improvements to the PESQ measure are reported along with new composite objective measures.

Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classical algorithms (e.g. spectral subtraction, Wiener filtering and Bayesian-based enhancement), and more recently several deep neural network-based. In this paper, we propose a hybrid approach to speech enhancement which combines two stages: In the first stage, the well-known Wiener filter performs the task of enhancing noisy speech. In the second stage, a refinement is performed using a new multi-stream approach, which involves a collection of denoising autoencoders and auto-associative memories based on Long Short-term Memory (LSTM) networks. We carry out a comparative performance analysis using two objective measures, using artificial noise added at different signal-to-noise levels. Results show that this hybrid system improves the signal's enhancement significantly in comparison to the Wiener filtering and the LSTM networks separately.