Minima Controlled Recursive Averaging Research Articles

A Multi-Channel Noise Estimator Based on Improved Minima Controlled Recursive Averaging for Speech Enhancement

A Multi-Channel Noise Estimator Based on Improved Minima Controlled Recursive Averaging for Speech Enhancement

Phase Noise Suppression for φ-OTDR With Optimized Minima Controlled Recursive Averaging Method

We propose a phase noise suppression method for phase-sensitive optical time-domain reflectometer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR) system. The signal-to-noise ratio (SNR) of the demodulated phase is improved by the optimized minima controlled recursive averaging (MCRA) method. First, the original phase waveform is divided into frames. Then, the minimum control is used to track the minimum power spectrum of phase noise with Gaussian distribution. The prior variance of noise is corrected by recursive averaging. The attenuation factor is determined by appropriate threshold parameters and the noise prior variance. Ultimately, the phase waveform is reconstructed in the time–frequency domain. In this way, the SNR of the reconstructed waveform is intensified remarkably. Experimental results show that the SNR of the demodulated phase is improved by 9.12–19.63 dB in the range of 20–1200 Hz. Furthermore, the enhancement of acoustic extracted from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR could also be verified experimentally with the proposed approach.

Robust unsupervised Tursiops aduncus whistle enhancement based on complete ensembled empirical optimal envelope local mean decomposition with adaptive noise.

Whistle enhancement is an essential preprocessing step in studying dolphin behavior and population distributions. We propose a robust unsupervised whistle enhancement scheme based on improved local mean decomposition using adaptive noise estimation and logarithmic spectral amplitude. First, to further mitigate the mode aliasing problem effect in whistle signal decomposition and achieve better spectral separation of modes, we present a complete ensembled empirical optimal envelope local mean decomposition with adaptive noise algorithm. According to the envelope characteristics of the whistle signals, the proposed algorithm optimally and adaptively decomposes the noisy signal into product functions (PFs) with amplitude and frequency modulation. Second, the whistle enhancement framework consists of the improved minima-controlled recursive averaging for adaptive noise estimation, optimally modified log-spectral amplitude for each noisy product function enhancement, and the Hurst index for reconstructing pure whistle signal estimations with the least damaged PFs. Finally, the proposed scheme is applied to a dataset of long calls from two Tursiops aduncus individuals. After constructing the pure whistle dataset, the experimental results show that the proposed scheme performs better than other compared whistle enhancement schemes under different signal-to-noise ratios.

On the Use of the AIRA-UAS Corpus to Evaluate Audio Processing Algorithms in Unmanned Aerial Systems.

Audio analysis over an Unmanned Aerial Systems (UAS) is of interest it is an essential step for on-board sound source localization and separation. This could be useful for search & rescue operations, as well as for detection of unauthorized drone operations. In this paper, an analysis of the previously introduced Acoustic Interactions for Robot Audition (AIRA)-UAS corpus is presented, which is a set of recordings produced by the ego-noise of a drone performing different aerial maneuvers and by other drones flying nearby. It was found that the recordings have a very low Signal-to-Noise Ratio (SNR), that the noise is dynamic depending of the drone’s movements, and that their noise signatures are highly correlated. Three popular filtering techniques were evaluated in this work in terms of noise reduction and signature extraction, which are: Berouti’s Non-Linear Noise Subtraction, Adaptive Quantile Based Noise Estimation, and Improved Minima Controlled Recursive Averaging. Although there was moderate success in noise reduction, no filter was able to keep intact the signature of the drone flying in parallel. These results are evidence of the challenge in audio processing over drones, implying that this is a field prime for further research.

Noise Suppression by Minima Controlled Recursive Averaging for SSVEP-Based BCIs With Single Channel

Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain–computer interface (BCI) application can effectively help subjects with ALS to participate in communication. In practices, the noise would greatly reduce the performance of BCIs. In this study, minima controlled recursive averaging is applied to suppress noise and improve the performance of practical BCI applications. Minima controlled recursive averaging is used to correctively track the noise. To suppress these noises, a log-spectral amplitude estimator is selected as the gain function and used to effectively estimate the power spectrum of the noises. Eight subjects were asked to attend a performance test of the proposed approach and the canonical correlation analysis (CCA) was adopted to compare the proposed approach. The average recognition rates based on single channel are 69.57% and 74.63% for CCA and proposed approach, respectively. The experimental results demonstrated that our approach is able to improve performance in practice.

Non-Stationary Noise PSD Matrix Estimation for Multichannel Blind Speech Extraction

Noise power spectral density (PSD) matrix estimation is one of the most important components of a multichannel blind speech extraction framework, as it largely determines the amount of residual noise at the output of a spatial filter. Optimality of well-known spatial filters, such as the multichannel Wiener filter, is only ensured if the PSD matrices of the noise and the desired speech are accurately estimated. In practical situations, where the noise is nonstationary, temporal averaging over time frames where the desired signal is inactive does not provide sufficiently fast tracking of the noise PSD matrix, resulting in high residual noise at the spatial filter output. Therefore, approaches that estimate the PSD matrices using narrowband signal detection have been proposed. Following the well-known single- and multichannel minima-controlled recursive averaging (MCRA) approaches, in this paper, we focus on narrowband speech presence probability-based noise PSD matrix estimators, which are suitable for blind scenarios where the location and the propagation vector of the desired speech source are unknown. The main contributions of the paper are a maximum likelihood interpretation of the multichannel MCRA, and a coherent-to-diffuse ratio-based a priori speech absence probability (SAP) estimator. The latter is a key parameter that determines the accuracy of the noise PSD matrix estimates in nonstationary scenarios. In this paper, we confirm the importance of the a priori SAP and show that its control is crucial for source extraction in nonstationary environments.

Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties

The accuracy of noise estimation is important for the performance of a speech denoising system. Most noise estimators suffer from either overestimation or underestimation on the noise level. An overestimate on noise magnitude will cause serious speech distortion for speech denoising. Conversely, a great quantity of residual noise will occur when the noise magnitude is underestimated. Accurately estimating noise magnitude is important for speech denoising. This study proposes employing variable segment length for noise tracking and variable thresholds for the determination of speech presence probability, resulting in the performance improvement for a minima-controlled-recursive-averaging (MCRA) algorithm in noise estimation. Initially, the fundamental frequency was estimated to determine whether a frame is a vowel. In the case of a vowel frame, the increment of segment lengths and the decrement of threshold for speech presence were performed which resulted in underestimating the level of noise magnitude. Accordingly, the speech distortion is reduced in denoised speech. On the contrary, the segment length decreases rapidly in noise-dominant regions. This enables the noise estimate to update quickly and the noise variation to track well, yielding interference noise being removed effectively through the process of speech denoising. Experimental results show that the proposed approach has been effective in improving the performance of the MCRA algorithm by preserving the weak vowels and consonants. The denoising performance is therefore improved.

Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement

In this paper, we demonstrate that spectral enhancement techniques can be configured to improve the classification accuracy of a pattern recognition-based myoelectric control system. This is based on the observation that, when the subject is at rest, the power in EMG recordings drops to levels characteristic of the noise. Two Minimum Statistics techniques, which were developed for speech processing, are compared against electromyographic (EMG) de-noising methods such as wavelets and Empirical Mode Decomposition. In the cases of simulated EMG signals contaminated with white noise and for real EMG signals with added and intrinsic noise the gesture classification accuracy was shown to increase. The mean improvement in the classification accuracy is greatest when Improved Minima-Controlled Recursive Averaging (IMCRA)-based spectral enhancement is applied, thus demonstrating the potential of spectral enhancement techniques for improving the performance of pattern recognition-based myoelectric control.

Audio bandwidth extension based on temporal smoothing cepstral coefficients

In this paper, we propose a wideband (WB) to super-wideband audio bandwidth extension (BWE) method based on temporal smoothing cepstral coefficients (TSCC). A temporal relationship of audio signals is included into feature extraction in the bandwidth extension frontend to make the temporal evolution of the extended spectra smoother. In the bandwidth extension scheme, a Gammatone auditory filter bank is used to decompose the audio signal, and the energy of each frequency band is long-term smoothed using minima controlled recursive averaging (MCRA) in order to suppress transient components. The resulting ‘steady-state’ spectrum is processed by frequency weighting, and the temporal smoothing cepstral coefficients are obtained by means of the power-law loudness function and cepstral normalization. The extracted temporal smoothing cepstral coefficients are fed into a Gaussian mixture model (GMM)-based Bayesian estimator to estimate the high-frequency (HF) spectral envelope, while the fine structure is restored by spectral translation. Evaluation results show that the temporal smoothing cepstral coefficients exploit the temporal relationship of audio signals and provide higher mutual information between the low- and high-frequency parameters, without increasing the dimension of input vectors in the frontend of bandwidth extension systems. In addition, the proposed bandwidth extension method is applied into the G.729.1 wideband codec and outperforms the Mel frequency cepstral coefficient (MFCC)-based method in terms of log spectral distortion (LSD), cosh measure, and differential log spectral distortion. Further, the proposed method improves the smoothness of the reconstructed spectrum over time and also gains a good performance in the subjective listening tests.

A novel speech enhancement using forward–backward minima-controlled recursive averaging

Nonstationary noises may seriously damage speech intelligibility and reduce the performance of hearing aids. This paper proposes a novel speech enhancement approach, named forward–backward minima-controlled recursive averaging, that helps to solve this problem. A forward–backward estimation is developed to correctively track nonstationary noises, especially the beginnings of nonstationary noises. To suppress these nonstationary noises, a log-spectral amplitude estimator was selected as the gain function for our approach and used to effectively estimate the power spectrum of the noise. Moreover, harmonic regeneration was integrated to reduce the overestimation of noises generated by the forward–backward estimation. The experimental results demonstrated that our approach is able to improve performance compared with previous approaches using both objective and subjective measures.

Noise estimation based on time–frequency correlation for speech enhancement

As a fundamental part of speech enhancement, noise estimation is particularly challenging in highly non-stationary noise environments. In this work, we propose an effective algorithm on the basis of the “Improved Minima Controlled Recursive Averaging (IMCRA)” with the objective to improve the performance of noise estimation. The main contributions of this work are: (i) in the algorithm, a rough decision about speech presence is proposed by calculating the autocorrelation and cross-channel correlation of the T–F (Time–Frequency) units; (ii) with this decision, we refine the smoothing parameters for the smoothing of noisy power spectrum and the recursive averaging in noise spectrum estimation as well as the weighting factor for the a priori SNR (Signal to Noise Ratio) estimation in the IMCRA; (iii) we improve the search of local minima during spectral bursts by adding a minimum search with a shorter window. Extensive experiments are carried out to evaluate the performance of our proposed algorithm. The experimental results illustrate that, compared with the IMCRA, the proposed approach significantly improves the accuracy of noise spectrum estimation and the quality of enhanced speech in the typical noise situations.

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.

Efficient Speech Enhancement Approach based on Minima Controlled Recursive Averaging through Modified Map Criterion using Hidden Markov Model

IJCA is a computer science and electronics journal related with Theoretical Informatics, Quantum Computing, Software Testing, Computer Vision, Digital Systems, Pervasive Computing, Computational Topology etc.

An Integrated Solution for Online Multichannel Noise Tracking and Reduction

Noise statistics estimation is a paramount issue in the design of reliable noise-reduction algorithms. Although significant efforts have been devoted to this problem in the literature, most developed methods so far have focused on the single-channel case. When multiple microphones are used, it is important that the data from all the sensors are optimally combined to achieve judicious updates of the noise statistics and the noise-reduction filter. This contribution is devoted to the development of a practical approach to multichannel noise tracking and reduction. We combine the multichannel speech presence probability (MC-SPP) that we proposed in an earlier contribution with an alternative formulation of the minima-controlled recursive averaging (MCRA) technique that we generalize from the single-channel to the multichannel case. To demonstrate the effectiveness of the proposed MC-SPP and multichannel noise estimator, we integrate them into three variants of the multichannel noise reduction Wiener filter. Experimental results show the advantages of the proposed solution.

A speech enhancement algorithm to reduce noise and compensate for partial masking effect

To enhance the speech quality that is degraded by environmental noise, an algorithm was proposed to reduce the noise and reinforce the speech. The minima controlled recursive averaging (MCRA) algorithm was used to estimate the noise spectrum and the partial masking effect which is one of the psychoacoustic properties was introduced to reinforce speech. The performance evaluation was performed by comparing the PESQ (perceptual evaluation of speech quality) and segSNR (segmental signal to noise ratio) by the proposed algorithm with the conventional algorithm. As a result, average PESQ by the proposed algorithm was higher than the average PESQ by the conventional noise reduction algorithm and segSNR was higher as much as 3.2 dB in average than that of the noise reduction algorithm.

Minima-controlled speech presence uncertainty tracking method for speech enhancement

In speech enhancement, soft decision, in which the speech absence probability (SAP) is introduced to modify the spectral gain or update the noise power, is known to be efficient. In many previous works, a fixed a priori probability of speech absence ( q) is assumed in estimating the SAP, which is not realistic since speech is quasi-stationary and may not be present in each frequency bin. To address this problem, Malah et al. devised a novel method to obtain distinct values of q for each frequency bin in many frames by comparing the a posteriori SNR to a threshold value [9]. In this regard, a novel algorithm is achieved by taking an advantage of a minima-controlled recursive averaging (MCRA) technique that allows for the robust tracking of speech absence in time. This leads to the improved tracking performance of speech absence in speech enhancement and better results in the objective and subjective evaluation tests.

Improved minima controlled recursive averaging technique using conditional maximum a posteriori criterion for speech enhancement

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) based on a conditional maximum a posteriori (MAP) criterion. From an investigation of the MCRA scheme, it is discovered that the MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. To avoid this phenomenon, the proposed MCRA approach incorporates the conditional MAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decision in the previous frame. Experimental results show that compared to the conventional MCRA method the proposed MCRA technique based on conditional MAP obtains low estimation error and when integrated into a speech enhancement system achieves improved speech quality.

A Speech Enhancement Algorithm based on Human Psychoacoustic Property

In the speech system, for example hearing aid as well as speech communication, speech quality is degraded by environmental noise. In this study, to enhance the speech quality which is degraded by environmental speech, we proposed an algorithm to reduce the noise and reinforce the speech. The minima controlled recursive averaging (MCRA) algorithm is used to estimate the noise spectrum and spectral weighting factor is used to reduce the noise. And partial masking effect which is one of the human hearing properties is introduced to reinforce the speech. Then we compared the waveform, spectrogram, Perceptual Evaluation of Speech Quality (PESQ) and segmental Signal to Noise Ratio (segSNR) between original speech, noisy speech, noise reduced speech and enhanced speech by proposed method. As a result, enhanced speech by proposed method is reinforced in high frequency which is degraded by noise, and PESQ, segSNR is enhanced. It means that the speech quality is enhanced.

Speech Enhancement Based on Minima Controlled Recursive Averaging Incorporating Second-Order Conditional MAP Criterion

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this letter, we propose a novel method to improve the minima controlled recursive averaging (MCRA) based on the second-order conditional maximum <emphasis emphasistype="italic">a posteriori</emphasis> (CMAP). From an investigation of the previous MCRA scheme, it is discovered that the MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on a current observation. To avoid this phenomenon, we propose the MCRA approach incorporating the second-order CMAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decision in the previous two frames. Experimental results show that the proposed MCRA technique based on second-order CMAP yields better results compared to the previous MCRA method in speech enhancement. </para>

Noise estimation by minima controlled recursive averaging for robust speech enhancement

In this letter, we introduce a minima controlled recursive averaging (MCRA) approach for noise estimation. The noise estimate is given by averaging past spectral power values and using a smoothing parameter that is adjusted by the signal presence probability in subbands. The presence of speech in subbands is determined by the ratio between the local energy of the noisy speech and its minimum within a specified time window. The noise estimate is computationally efficient, robust with respect to the input signal-to-noise ratio (SNR) and type of underlying additive noise, and characterized by the ability to quickly follow abrupt changes in the noise spectrum.