Input SNR Levels Research Articles

The speech signal enhancement approach with multiple sub-frames analysis for complex magnitude and phase spectrum recompense

The intended speech must be dealt with in the process of speech communication while under the impact of noise experienced in a variety of situations that degrade speech intelligibility and quality. This work proposes a multiple sub-frames analysis for the elimination of noise variants with compensation of the magnitude and phase spectrum from speech degraded by noise. The clean speech samples are extracted from the ITU-T recommended dataset at a 16 kHz sampling rate and down-sampled to an 8 kHz sampling rate. The noise signal variants are added from the AURORA and NOISEX-92 datasets at diverse input SNR levels (0 dB, 5 dB, 10 dB, 15 dB). The duration of window frames is chosen to be 25 msec in length, together with a shift percentage of 40%, to maintain the continuous nature of frames in speech. The smoothing factor for noise updating in a specific sub-frame is set to 9, and the spectral floor parameter for determining the precise amount of noise elimination in the corrupted spectrum is set to 0.03. The phase spectrum is compensated by incorporating a recompense function that is updated in combination with the sub-frame analysis. The accomplishment of the suggested approach is assessed with regard to objective metrics, including Segmental-Signal-to-Noise-Ratio (SegSNR), Mean-Square-Error (MSE), and Perceptual-Evaluation-of-Speech-Quality (PESQ) scores corresponding to specific sub-frames of speech, respectively. To further analyze the improved quality, simple listening assessment and spectrogram analysis are incorporated, followed by a comparative investigation with prior noise-suppressive algorithms on the corrupted speech corpus.

Noise reduction in speech signal of Parkinson’s Disease (PD) patients using optimal variable stage cascaded adaptive filter configuration

In hands-free communications, including mobile phones, teleconferencing, and video conferencing, the ultimate aim is to obtain a noise-free signal. The adaptive filters that utilize the LMS adaptation algorithm are the best suited for speech signal de-noising among the existing speech signal enhancement techniques. LMS adaptive filter is preferred due to its low computational complexity and robustness, but it fails to simultaneously achieve a faster convergence speed and minimum steady-state MSE. This work introduces a novel cascaded adaptive filter structure for speech signal de-noising wherein the objective is to estimate the noise-free signal with high accuracy. The proposed optimal variable stage cascaded adaptive filter model utilizes a variable stage cascaded adaptive filter structure to estimate the noise-free signal, which is then employed to recover the clean signal with high accuracy. The proposed filter model is tested for reducing the noise from a normal speech signal taken from the NOIZEUS database and an impaired speech signal of Parkinson’s Disease affected patients taken from the MDVR-KCL dataset. The signals are corrupted by various stationary and non-stationary noise signals of different input SNR levels. The noisy signal is used to test the performance of the proposed optimal filter model. The signal de-noising performance of the proposed filter is evaluated in MSE, SNR, and ANR. The results have shown that the proposed filter model provides remarkable performance and an output SNR of 10–15 dB higher than the existing cascaded filter structures. Further, the proposed filter model employs LMS adaptive algorithm, offering a cost-effective and straightforward hardware implementation of ANC with improved accuracy.

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal.

Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.

DNN-Based Calibrated-Filter Models for Speech Enhancement

In this paper, we present a new two-stage speech enhancement approach, specially conceived to reduce musical and other random noises without requiring their localization in the time–frequency domain. The proposed method is motivated by two observations: (1) the random scattering nature of the energy peaks corresponding to the musical noise in the spectrogram of the processed speech; and (2) the existence of correlation between Wiener filter gains calculated at different frequencies. In the first stage of the proposed method, a preliminary gain function is generated using the nonnegative matrix factorization algorithm. In the second stage, a modified gain function that is more robust to noise artefacts, and referred to as calibrated filter, is estimated by applying a DNN-based nonlinear mapping function to the preliminary gain function. To further decrease the variability of the estimated calibrated filter, we propose to expand the DNN-based extraction of frequency dependencies to a set of preliminary gain functions derived from spectral estimates based on a family of data tapers; the resulting calibrated filter is referred to as multi-filter. The evaluation of the proposed DNN-based calibrated filter models for speech enhancement, under different noise types and input SNR levels, shows substantial improvements in terms of standard speech quality and intelligibility measures when compared to uncalibrated filter.

New optimal variable step size-adaptive regularized-affine projection algorithm

In this paper, a new optimal variable step size-adaptive regularized-affine projection algorithm (OVSS-AR-APA) is proposed and compared with existing variable step size APA family of algorithms. Instead of having a constant regularization parameter, the modified sigmoid function variation is used to choose the optimum regularization parameter in the proposed algorithm. Here, the algorithm dynamically adjusts the regularization parameter according to input noise variations. Also, the exponentially weighted value of the error with variable regularization parameter is used for making the step size parameter variable. The existing and proposed algorithms are implemented for noise cancellation from audio signals at various input SNR levels for different filter orders. It is observed from simulations that the proposed algorithm outperforms the existing algorithms in terms of SNR improvements, MSE and convergence rate at all input SNR levels for different filter orders with moderate computational complexity. The OVSS-AR-APA algorithm shows a maximum of 21.54 dB of improvement in SNR at -20dB input SNR level at filter order 5.

Performance Analysis of MSFRLS-VFF Based Real-Time Adaptive Noise Canceller with RLS and APA Algorithms Using TMS320C6713 Processor

In this paper, the real-time adaptive noise canceller (ANC) system is implemented using modified sigmoid function variation based recursive least square with variable forgetting factor (MSFRLS-VFF) algorithm. The experiment is performed on DSP TMS320C6713. The performance of MSFRLS-VFF algorithm is evaluated and comparison is made with conventional RLS and affine projection algorithm (APA). In the experimental setup, the different types of noises are artificially added into the clean signals to make the signal noisy at different input SNR levels (− 5 dB to 10 dB). The result shows that MSFRLS-VFF algorithm provides superior performance than RLS and APA algorithm (the best performance achieved for SNR improvement is 2.2 dB over RLS and 2.1 dB over APA at − 5 dB input SNR with filter order 10). Also, the MSFRLS-VFF algorithm provides minimum mean square error than RLS and APA algorithms, however computational complexity of APA algorithm is less as compared to MSFRLS-VFF and RLS algorithms. The output error-free signal obtained after MATLAB simulation and from TMS320C6713 DSP shows the similar result that proves the correctness of the setup.

The SPURS Algorithm for Resampling an Irregularly Sampled Signal onto a Cartesian Grid.

We present an algorithm for resampling a function from its values on a non-Cartesian grid onto a Cartesian grid. This problem arises in many applications such as MRI, CT, radio astronomy and geophysics. Our algorithm, termed SParse Uniform ReSampling (SPURS), employs methods from modern sampling theory to achieve a small approximation error while maintaining low computational cost. The given non-Cartesian samples are projected onto a selected intermediate subspace, spanned by integer translations of a compactly supported kernel function. This produces a sparse system of equations describing the relation between the nonuniformly spaced samples and a vector of coefficients representing the projection of the signal onto the chosen subspace. This sparse system of equations can be solved efficiently using available sparse equation solvers. The result is then projected onto the subspace in which the sampled signal is known to reside. The second projection is implemented efficiently using a digital linear shift invariant (LSI) filter and produces uniformly spaced values of the signal on a Cartesian grid. The method can be iterated to improve the reconstruction results. We then apply SPURS to reconstruction of MRI data from nonuniformly spaced k-space samples. Simulations demonstrate that SPURS outperforms other reconstruction methods while maintaining a similar computational complexity over a range of sampling densities and trajectories as well as various input SNR levels.

An Efficient Method of ECG Signals Denoising Based on an Adaptive Algorithm Using Mean Filter and an Adaptive Dual Threshold Filter

This paper is proposing an efficient denoising method of baseline wandering and high frequency noise for ECG signals. The proposed method starts by extracting baseline wandering from ECG signal. This technique has been developed using an adaptive algorithm based on mean filter. Next, an adaptive dual threshold filter is proposed to deal with high frequency noises. This filter is inspired from an adaptive dual threshold median filter recently developed in image processing. This paper presents certain applications of this algorithm on some of the MIT-BIH Arrhythmia database’s signals. Interestingly, results of this method are very promising compared to recently published methods. In the case of an input SNR level of 5dB to 20dB, the values of the PRD parameter are varied between 25 % and 7 %. For the same input SNR level, the obtained values of the MSE parameter are varied between 0.0087 and 0.00062. The aim of this work, in addition to improving denosing, is to implement this method in real-time systems, which is the next step of this work.

A comparative study of BA, APSO, GSA, hybrid PSOGSA and SPSO in dual channel speech enhancement

The present paper focuses on the suppression of background noise in speech signal by utilizing a powerful heuristic optimization algorithm called Bat algorithm (BA) for adaptive filtering in dual channel enhancement systems. Bat algorithm is a recently developed population based meta heuristic approach which is inspired by the hunting behavior of the bats. It is developed by Yang (2010). As a novel feature Bat algorithm is based on the echo location behavior of micro bats. BA uses the frequency tuning technique to increase the diversity of the solutions in the population, while at the same time it uses the automatic zooming and tries to balance the exploration and exploitation. A few studies have been carried out on the use of heuristics for ANC in speech enhancement by using standard particle swarm optimization (SPSO) and some of its variants till 2010. In order to extend these heuristic approaches to speech, accelerated particle swarm optimization (APSO) based enhancement approach has been proposed (Prajna et al. in Int J Speech Technol 17(4):341---351, 2014a; Prajna et al. in IJISA 6(4):1---10, 2014b, doi:10.5815/ijisa.2014.04.01; Prajna et al. in IEEE international conference on communications and signal processing (ICCSP), April 2014, pp 1457---1461, 2014c, doi:10.1109/ICCSP.2014.6950090). To overcome the problem of poor exploitation ability of APSO, another approach is proposed based on gravitational search algorithm (GSA) (Prajna et al. 2014a, b, c). To combine both the abilities of PSO and GSA algorithms, Hybrid PSOGSA algorithm is also proposed to ANC (Prajna et al. 2014a, b, c). To further improve the efficiency of adaptive filtering in ANC, by providing a dynamic balance between exploration and exploitation, BA is proposed to speech enhancement. This paper intends to present the Bat algorithm as an improved approach to ANC in speech enhancement when compared with that of SPSO, APSO, GSA and Hybrid PSOGSA based speech enhancement algorithms. The performance of all the algorithms is evaluated by computing four objective measures SNRI, PESQ, FAI and WSS, in two real world noise conditions Babble and Factory, at three different input SNR levels set at ?10, 0 and 5 dB. Simulation results prove that BA is the most successful algorithm of all the algorithms studied in this work, to suppress the background noise more effectively.

Speech Enhancement Using Modified Modulation Magnitude Estimation-Based Spectral Subtraction Algorithm

In this paper, noise variance is estimated from the noisy spectrum using the proposed noise estimation algorithm. In addition, the speech signal is enhanced by the modified modulation magnitude estimation (MMME)-based spectral subtraction algorithm. Chi-square distribution-based noise estimation algorithm is proposed in this work. During the speech presence period, the noise variance is estimated by recursively smoothing and averaging the noisy spectrum. The speech signal is then enhanced by estimating the modulation magnitude spectrum, which depends on a posterior SNR instead of a priori SNR. The optimal speech presence probability q m is found as 0.9, and simulations are carried out for this optimal value. Objective and subjective measures for the various noises are made and compared between the existing and proposed algorithms. From the experimental results, it is observed that the proposed noise estimation method comparably reduces mean square error (MSE) and LogErr to the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed spectral subtraction method noticeably increases the dB value of peak signal to noise ratio, segmental SNR and segmental SNR improvement (ΔSNRseg) contrasting various existing spectral subtraction algorithms with the optimal q m value. Also, it improves the mean opinion score value and reduces the log spectral distance and MSE when compared to that of various existing ones. Hence, it reveals that the MMME-based spectral subtraction with proposed noise estimation algorithm reduces the speech distortion and residual noise as compared to existing methods.

A new approach to dual channel speech enhancement based on gravitational search algorithm (GSA)

This paper proposes novel heuristic algorithm called gravitational search algorithm (GSA) to speech enhancement. Stochastic and heuristic algorithms like particle swarm optimization (PSO) and some of its variants have been adapted to the field of speech enhancement in recent years. Although standard PSO (SPSO) finds good solutions, it suffers from the premature convergence by getting trapped into local optimum. In order to increase the diversity in search space and to improve the local searching capability, another recently developed heuristic algorithm GSA is proposed to speech enhancement in this paper. GSA is mainly constructed on the basis of law of gravity and the notion of mass interactions .The proposed algorithm is studied for real world noise condition called babble noise, at three different input SNR levels. To the best of our knowledge, there is no analysis about the intelligibility of enhanced speech using optimization techniques. In the present study, the proposed algorithm is compared with the standard PSO (SPSO) algorithm for dual channel speech enhancement, and the intelligibility analysis is also reported. Simulation results indicate that GSA-based algorithm outperforms the particle swarm optimization in adaptive noise cancellation with an improved speech quality and intelligibility.

I/Q Imbalance in Multiple Beamforming {OFDM} Transceivers: SINR Analysis and Digital Baseband Compensation

In this paper, we start by investigating the impact of joint transmit-receive I/Q imbalance on the performance of direct-conversion beamforming OFDM transceivers. We derive a new analytical expression for the average subcarrier SINR of the I/Q imbalance-ignorant beamformer in terms of the I/Q imbalance level at both the transmit and receive sides, the size of the beamforming array, and the input SNR level. This expression motivates the need for I/Q imbalance compensation and provides valuable design insights when examining several special and limiting cases. Next, we derive the throughput-maximizing multiple beamforming transmit/receive coefficients under a general system model with an asymmetric frequency-dependent (FD) joint transmit-receive I/Q imbalance. In addition, we propose a low-complexity pilot-aided scheme for the estimation of the channel and I/Q imbalance parameters. Our simulation results demonstrate that the proposed generalized multiple beamforming scheme is highly effective in mitigating I/Q imbalance effects at practical complexity levels.

Detection Performance of Polarization and Spatial Diversities for Indoor GNSS Applications

Multipath fading in the form of signal power fluctuation poses a formidable challenge to GNSS signal detection in harsh multipath environments such as indoors. Antenna diversity techniques such as polarization and spatial diversities can be used to combat multipath fading in wireless propagation channels. This paper studies and compares GPS signal detection performance enhancements arising from the spatial and polarization diversity techniques. Performance enhancements are quantified from a theoretical perspective and later verified based on several test measurements in various indoor environments. Enhancement is quantified based on measuring the correlation coefficient values between diversity branches, SNR levels, and computing the level crossing rate and average fade duration. In addition, the processing gain is quantified and the performance of each individual diversity system is evaluated. Experimental results show that, for a given target detection performance in terms of the probability of false alarm and the probability of detection, the required input SNR level to meet the target detection performance can be significantly reduced utilizing the diversity system.