Non-stationary Noisy Environments Research Articles

Noise estimation based on optimal smoothing and minimum controlled through recursive averaging for speech enhancement

One of the most significant challenges in the real time speech processing applications is the elimination of noise in the corrupted speech data. This noise can significantly impact the efficacy/performance of the applications of speech processing. However, developing a robust noise reduction algorithm is crucial for improving the accuracy of automatic speech recognition (ASR) and other speech processing systems under uncontrolled conditions. In this paper, we propose an algorithm to reduce the background noise in the degraded speech data under highly non-stationary conditions. The proposed optimal smoothing and minima controlled (OSMC) technique uses recursive averaging to enhance degraded speech data. Initially, a smoothed periodogram and local minima of the degraded speech data are computed and determined the time-frequency dependent threshold factor. The ratio of smoothed periodogram to local minima is used to find the active regions of speech in the degraded speech data by adapting the Bayesian minimum cost decision rule. To calculate the estimated noise spectrum for each frequency bin, a time-frequency smoothing factors are used. The perceptual evaluation of speech quality (PESQ) and normalized covariance metric (NCM) are used to evaluate the speech quality and intelligibility of the proposed technique over competing algorithms after speech enhancement. The experimental results demonstrated that the proposed algorithm has given a significant improvement in terms of average values of PESQ by 15.03% and 16.71% and NCM by 3.45% and 5.73% for NOIZEUS and Kannada speech databases at 5 dB and 10 dB respectively, over unprocessed speech under highly non-stationary noisy environments.

Smart Emotion Recognition Framework: A Secured IoVT Perspective

The promise of automated-driving cars causes the automotive and consumer electronics (CE) sector to rethink not only what it means to drive, but also the relationship between the car and the consumer. Recent trend in Internet of Vehicle Things (IoVT) promotes robust interactions between humans and vehicles, which ultimately points to enhance human abilities, such as hearing, visual surveillance, or emotion awareness, as a part of safety concern. The voice-based interactions (speech recognition and stress monitoring) will improve in-time awareness of the vehicle status. Unfortunately, the existing modulation domain speech enhancement techniques achieve low satisfactory performance in detecting humans’ stress emotions where the environmental noise is inevitable and varies with the location of every passing vehicle. Furthermore, the computational load introduces challenges in their implementation in automated vehicles. In this direction, we propose a front-end processing framework, in particular to stress emotion detection cases (such as anger, sad, fear, and happy) in different nonstationary noisy environments, such as car, airport, traffic, and train. This article encompasses three interrelated issues: 1) analysis, modification, and synthesis of noisy speech emotion in modulation domain in real-time background noise, 2) extracting set of Mel-frequency cepstral coefficients features from noisy speech stimuli for speech emotion recognition, and 3) evaluation of overall system performance by means of objective parameters, and confusion matrix in adverse environments using speech emotion database Interactive Emotional Dyadic Motion Capture. The experimental results show that favorable performance in state-of-the-art stress monitoring yields high levels of consumer satisfaction for security in vehicle comparison to traditional frameworks.

Real Time Implementation of Voice Activity Detection based on False Acceptance Regulation

In speech compression systems, Voice Activity Detection (VAD) is frequently used to distinguish active voice from other noisy sounds. In this paper, a robust approach of VAD is presented to deal with non-stationary noisy environments. The proposed algorithm exploits adaptive thresholding technique to keep a desired False Acceptance (FA) rate. Iterative hypothesis tests, using signal energy, are implemented to discard or to accept the successive audio frames as active voice. According to the stationary property of the speech, we provide a smoothing method to obtain final VAD decisions. The main contribution of the proposed algorithm concerns its ability to automatically adjust the energy threshold according to the local noise estimator. We analyzed the proposed approach by presenting a comparison with the G.729-B via the NOIZEUS database. The VAD architecture is implemented on a Microcontroller-based system (MCU). Several tests have been conducted by performing real time acquisition via the Input/Output ports of the MCU-system.

Probability decision‐driven speech enhancement algorithm based on human acoustic perception

In this study, a novel human acoustic perception motivated Wiener filter speech enhancement system is presented to cope with real-world interfering background noises. Guiding by the speech presence probability, two alternative methods are proposed to reduce the noise by adopting the audible sound pressure level (SPL) and the masking characteristic of the human auditory system to achieve better listening comfort level. More specifically, when the probability of speech presence in the noisy signal is less than the decision threshold, a new SPL compressed method effectively reduces the noise. When the speech presence probability is more than the decision threshold, an improved acoustical mask threshold constrained Wiener filter approach enhances the noisy speech. Moreover, in order to evaluate the performance of the new system, the proposed algorithm is compared with the classic prior signal-to-noise ratio-based Wiener filter and three acoustic perception related algorithms. The experimental results show that the proposed algorithm significantly outperforms the four comparing algorithms in terms of speech quality and intelligibility either in stationary or moderate non-stationary noisy environments. Thus, the intended approach can be employed as the front-end module for various speech-related applications.

Speech denoising using Bayesian NMF with online base update

A new speech denoising method based on online non-negative matrix factorization (NMF) is proposed in this paper. To achieve an efficient model for the temporal dependencies of speech and noise, and to improve the robustness for the actual non-stationary noisy environments, the Bayesian NMF is extended to the proposed model and a new noise basis matrix online update method is exploited. Firstly, the speech basis matrix is pre-trained off-line with the Bayesian NMF method. In speech denoising stage, the noise basis matrix is continuously updated by utilizing the noise frames in the noisy observation with the Bayesian NMF. The noise basis matrix is initialized via a pre-trained universal noise NMF model and the noise data for the matrix adaption are selected using a likelihood ratio test (LRT) speech decision criterion. Then the updated noise basis matrix and the pre-trained speech basis matrix are employed to the enhancement of the noisy signal. Finally, to address the incomplete separation and the speech distortion problem, a speech activity probability based noise suppression filter is presented to further eliminate the residue noise in the enhanced result. The experiment results show that the proposed method outperforms the comparative denoising algorithms in terms of objective measurement.

Unsupervised speech enhancement in low SNR environments via sparseness and temporal gradient regularization

A crucial stage in unsupervised speech enhancement algorithm is the estimation of noise related parameters which usually needs prior models for noise. However, estimation of such parameters is a challenging task at low signal-to-noise ratios or in nonstationary noisy environments. In this paper, without knowing the prior models, an unsupervised and iterative speech enhancement algorithm is proposed which assumes speech spectrogram and its temporal gradient as sparse components. The quasi-harmonic description of the speech signals justifies this assumption. The speech enhancement is performed by decomposing the spectrogram of noisy speech into sparse matrix, enforcing the sparsity and temporal gradient regularizations. The Kullback–Leibler divergence is incorporated to minimize the distance between the observation and reconstructed components with nonnegativity constraints. Alternating direction method of multipliers is used to optimize the algorithm. The proposed algorithm is different from many speech enhancement approaches as it reduces background noise in an uncomplicated manner without need of a noise estimation algorithm to find noise-only excerpt. In addition, the proposed algorithm obtains an improved performance in adverse environments without knowing the exact distribution of noise. The experimental results demonstrate that the proposed algorithm outperforms the competing algorithms in terms of the speech quality and intelligibility. Moreover, the composite objective measure reinforced better performance in terms of residual noise and speech distortion in strong noise.

A New Hybridized Speech Enhancement Technique for Stationary and Non-Stationary Noisy Environments

A New Hybridized Speech Enhancement Technique for Stationary and Non-Stationary Noisy Environments

Speech Enhancement Using Multi‐channel Post‐Filtering with Modified Signal Presence Probability in Reverberant Environment

In this paper, a multi-channel post-filtering approach in reverberant environment based on detection and estimation scheme is presented. A modified Signal presence probability (SPP), which is in consideration of reverberation, is proposed with a novel estimator Direct-toreverberate ratio (DRR) to adapt to distant-talking scene. SPP is known a key estimator to instruct the updating of transient noise or residual directional interference and form gain function in the time-frequency domain, consequently a new desired signal detection scheme is proposed to improve its accuracy. Appropriate spectral enhancement technique is applied to the noisy speech signal taking advantage of the modified SPP estimator. The proposed multi-channel post-filtering is tested in different nonstationary noisy and reverberant environments. Experimental results show that it achieves considerable improvement on signal preservation of the desired speech with more noise reduction over the comparative algorithms.

Noise Estimation and Suppression Using Nonlinear Function withA PrioriSpeech Absence Probability in Speech Enhancement

This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function anda priorispeech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstationary noisy environments; however, it tends to bias noise estimation below that of the true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function anda prioriSAP for residual noise reduction. Additionally, our method uses an autoparameter to control the trade-off between speech distortion and residual noise. We evaluate the estimation of noise power in highly nonstationary and varying noise environments. The improvement can be confirmed in terms of signal-to-noise ratio (SNR) and the Itakura-Saito Distortion Measure (ISDM).

Performance Improvement of Digital Hearing Aid Systems

Digital hearing aids addresses the issues of noise and speech intelligibility that is associated with the analogue types. One of the main functions of the digital signal processor (DSP) of digital hearing aid systems is noise reduction which can be achieved by speech enhancement algorithms which in turn improve system performance and flexibility. However, studies have shown that the quality of experience (QoE) with some of the current hearing aids is not up to expectation in a noisy environment due to interfering sound, background noise and reverberation. It is also suggested that noise reduction features of the DSP can be further improved accordingly. Recently, we proposed an adaptive spectral subtraction algorithm to enhance the performance of communication systems and address the issue of associated musical noise generated by the conventional spectral subtraction algorithm. The effectiveness of the algorithm has been confirmed by different objective and subjective evaluations. In this study, an adaptive spectral subtraction algorithm is implemented using the noise-estimation algorithm for highly non-stationary noisy environments instead of the voice activity detection (VAD) employed in our previous work due to its effectiveness. Also, signal to residual spectrum ratio (SR) is implemented in order to control the amplification distortion for speech intelligibility improvement. The results show that the proposed scheme gives comparatively better performance and can be easily employed in digital hearing aid system for improving speech quality and intelligibility.

Combined adaptive beamforming schemes for nonstationary interfering noise reduction

This paper introduces new adaptive beamforming methods for nonstationary noise reduction, designed to be robust against broadband interfering signals. In particular, we propose combined beamforming schemes within a standard adaptive beamforming system, such as the generalized sidelobe canceller (GSC). The novelty of such combined adaptive beamformers relies on the use of different adaptive sidelobe cancelling structures which allow the system to achieve robustness in nonstationary noisy environments. The combined structures are based on the convex combination of two multiple-input single-output (MISO) adaptive systems with complementary capabilities. The whole beamformer benefits from such combination and results to be able to preserve the best properties of each system. We introduce two different adaptive schemes, whose difference lies in the way of combining the MISO systems. Moreover, we present a further adaptive beamforming scheme which generalizes the previous techniques, thus improving the robustness against nonstationary interfering signals in multisource environments. The effectiveness of the proposed systems is also assessed in a nonstationary dense multipath environment. The experiments show that the proposed combined beamforming schemes are capable of enhancing the desired signal even in the presence of nonstationary interfering signals.

MCRA noise estimation for KLT-VRE-based speech enhancement

A new signal subspace-based approach is proposed for the enhancement of speech corrupted by a high level of noise. Conventional subspace-based methods use the minimum mean square error criterion to optimize the Karhunen-Loeve Transform (KLT). In non-stationary noisy environments, the selection of the optimal order of the KLT-based speech enhancement model is a critical issue. Indeed, estimation of the relevant subspace dimensions depends on the environmental conditions that may change unpredictably. Therefore, a drastic KLT-based dimension reduction may induce the loss of relevant components of speech and conversely, a reconstruction using a higher order of the KLT model will be ineffective to remove the noise. The method presented in this paper uses a Variance of Reconstruction Error (VRE) criterion to optimally select the KLT order model. A prominent point of this subspace method is that it incorporates the Minima Controlled Recursive Averaging (MCRA) to estimate the noise Power Spectral Density (PSD) used in the gain function. Three variants of the VRE combined with MCRA methods are implemented and compared, namely the VRE-MCRA, VRE-MCRA2 and VRE-IMCRA. Objective measures show that VRE-based approaches achieve a lower signal distortion and a higher noise reduction than existing enhancement methods.

잡음 환경 분류 알고리즘을 이용한 IMCRA 기반의 음성 향상 기법

In this paper, we propose a novel method to improve the performance of the improved minima controlled recursive averaging (IMCRA) in non-stationary noisy environment. The conventional IMCRA algorithm efficiently estimate the noise power by averaging past spectral power values based on a smoothing parameter that is adjusted by the signal presence probability in frequency subbands. Since the minimum of smoothing parameter is defined as 0.85, it is difficult to obtain the robust estimates of the noise power in non-stationary noisy environments that is rapidly changed the spectral characteristics such as babble noise. For this reason, we proposed the modified IMCRA, which adaptively estimate and updata the noise power according to the noise type classified by the Gaussian mixture model (GMM). The performances of the proposed method are evaluated by perceptual evaluation of speech quality (PESQ) and composite measure under various environments and better results compared with the conventional method are obtained.

Two-Microphone Noise Reduction Using Spatial Information-Based Spectral Amplitude Estimation

SUMMARY Traditionaltwo-microphonenoisereductionalgorithmstodeal with highly nonstationary directional noises generally use the direc-tion of arrival or phase difference information. The performance of thesealgorithms deteriorate when diffuse noises coexist with nonstationary di-rectional noises in realistic adverse environments. In this paper, we presenta two-channel noise reduction algorithm using a spatial information-basedspeech estimator and a spatial-information-controlled soft-decision noiseestimator to improve the noise reduction performance in realistic non-stationary noisy environments. A target presence probability estimatorbased on Bayes rules using both phase difference and magnitude squaredcoherenceisproposedforsoft-decisionofnoiseestimation,sothattheycanshare complementary advantages when both directional noises and diffusenoises are present. Performances of the proposed two-microphone noisereduction algorithm are evaluated by noise reduction, log-spectral distance(LSD) and word recognition rate (WRR) of a distant-talking ASR systemin a real room’s noisy environment. Experimental results show that theproposed algorithm achieves better noises suppression without further dis-torting the desired signal components over the comparative dual-channelnoise reduction algorithms.

Speech enhancement based on modified a priori SNR estimation

To solve the frame delay problem and match the previous frame, Plapous et al. [IEEE Transactions on Audio, Speech, and Language Processing, 2006, 14(6): 2098–2108] introduced a novel approach called two-step noise reduction (TSNR) technique to improve the performance of the speech enhancement system. However, TSNR approach results in spectral peaks of short duration and the broken spectral outlier, which degrade the spectral characteristics of the speech. To solve this problem, a cepstral smoothing step is added in order to remove these spectral peaks brought by TSNR approach. Theory analysis shows that the proposed approach can effectively smooth the spectral peaks and keep the spectral outlier so as to protect the speech characteristics. Experiment results also show that the proposed approach can bring significant improvement compared to decision-directed (DD) and TSNR approaches, especially in non-stationary noisy environments.

Automotive speech control in a non-stationary noisy environment

This paper presents a novel in-car design to recognise the driver's voice and to control in-car devices. In a non-stationary noise car environment, a speech recognition kit SR-07 with speech recognition chip HM2007 has less than 10% successful hit-rate. With an adaptive Wiener filter, the successful hit-rate has improved to as much as 75%.

Voice Driven Applications in Non-stationary and Chaotic Environment

Automated operations based on voice commands would become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under nonstationary and chaotic noise environments. In this research, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of non-stationary noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of signal-to-noise ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are from 80% to 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out

Comparison of visual features for audio‐visual speech recognition using the AURORA‐2J‐AV database

Various techniques for noise‐robust speech recognition have been developed for many years. However, speech recognition in nonstationary noisy environments, such as in car conditions, is still a very challenging research problem. Audio‐visual speech recognition is a promising technique for improving noise robustness in such adverse acoustic conditions. Our research group has recently developed an audio‐visual speech database ‘‘AURORA‐2J‐AV’’ following the AURORA2 database protocol, which has been widely used for evaluating the performance of audio‐only speech recognition techniques. The AURORA‐2J‐AV database consists of audio speech signals and two types of image sequences: color and infrared human face images recorded when multi‐digit numbers are spoken by about 100 native Japanese speakers. Visual noise as well as acoustic noise is added to the database after recording for simulating various conditions. Audio‐visual speech recognition experiments are conducted using the audio speech and infrared images of the AURORA‐2J‐AV database. Two visual features, correlation coefficients (CCs) between two successive image frames and principal component scores (PCSs) of lip images, are compared in various signal‐to‐noise ratio (SNR) conditions. Experimental results show that CCs outperform PCSs at high SNRs, while PCSs function better at lower SNRs.

Target sound extraction utilizing similarity between signals as index for learning of linear filter

In this paper, we propose a novel learning method of two-channel linear filter for a target sound extraction in a non-stationary noisy environment using a two-channel microphone array. The method is based on a correlation coefficient between received sounds of two microphones. The cue signal, which has a correlation with a variation of S/N of the received sounds, is generated using the correlation coefficient and is applied to the learning. By several computer simulation results, a superior performance of the proposed method even at the consonant section of the speech signal is presented in comparison with the previously proposed method. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 45–52, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20141

Signal detection for frequency-shift keying via short-time stochastic resonance

A series of short-time stochastic resonance (SR) phenomena, realized in a bistable receiver, can be utilized to detect a train of information represented by signals that adopt frequency-shift keying (FSK). It is demonstrated that the values of noise intensity at resonance regions are close for adjacent periodic signals with an appropriate frequency separation. This establishes the possibility of decoding subthreshold or slightly suprathreshold M-ary FSK signals in bistable receivers. Furthermore, the mechanism of FSK signal detection via short-time SR effects is elucidated in terms of the receiver response speed. This phenomenon provides a possible mechanism for information processing in a bistable device operating in nonstationary noisy environments, where even the inputs appear over a short timescale or have a frequency shift.