Perceptual Evaluation Of Speech Quality Score Research Articles

Automatic bioacoustics noise reduction method based on a deep feature loss network

Acoustic sensors that collect acoustic data over extended periods and broad ranges are widely used in bioacoustics monitoring. However, in open environments, acoustic data collected using acoustic sensors can be subject to interference from various real-world noises, thereby influencing the subsequent analysis and processing of bioacoustic data. Existing bioacoustic noise reduction methods are limited in their application because of their low efficiency, unsuitability for non-stationary noise, generally unimproved signal-to-noise ratio (SNR) efficacy, and considerable amounts of residual noise. These limitations hinder the effective processing of recorded signals for which extraneous noise overlaps with bird vocalizations. In this study, we propose a bioacoustic noise reduction method based on a deep feature loss network for bird sounds. The method has a rapid denoising speed and can more effectively remove background noise from field recording signals without distorting the bird acoustic spectrum. The denoising effects of the proposed method were compared with those of a speech enhancement generative adversarial network, web real-time communications denoising, and other noise reduction methods. The denoising ability of these methods for different noises was evaluated using spectrograms and objective evaluation measures such as the SNR and perceptual evaluation of speech quality (PESQ). The experimental results revealed that our proposed noise reduction method can obtain higher SNRs and PESQ scores than other noise reduction methods, with the SNR increasing by up to 35.83 dB following denoising.

On intrusive speech quality measures and a global SNR based metric

Measuring the quality of noisy speech signals has been an increasingly important problem in the field of speech processing as more and more speech-communication and human-machine-interface systems are deployed in practical applications. In this paper, we study four widely used classical performance measures: signal-to-distortion ratio (SDR), short-time objective intelligibility (STOI), signal-to-noise ratio (SNR), and perceptual evaluation of speech quality (PESQ). Through analyzing these performance measures under the same framework and identifying the relationship between their core parameters, we convert these measures into the corresponding equivalent SNRs. This conversion enables not only some new insights into different quality measures but also a way to combine these measures into a new metric. In the derivation of the equivalent SNRs, we introduce the widely used masking technique into the computation of correlation coefficients, which is subsequently used to analyze STOI. Furthermore, we propose an attention method to compute the core parameters of PESQ, and also an empirical formula to project the equivalent SNRs into PESQ scores. Experiments are carried out and the results justifies the properties of the derived quality measures.

New research on monaural speech segregation based on quality assessment

Speech enhancement (SE) is a pivotal technology in enhancing the quality and intelligibility of speech signals. Nevertheless, when processing speech signals under conditions of high signal-to-noise ratio (SNR), conventional SE techniques may inadvertently lead to a diminution in the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). This article introduces the innovative incorporation of the Non-Intrusive Speech Quality Assessment (NISQA) algorithm into SE systems. Through the comparison of pre and post-enhancement speech quality scores, it discerns whether the speech signal under consideration warrants enhancement processing, thereby mitigating potential deterioration in PESQ and STOI. Furthermore, this study delves into the ramifications of five prevalent speech features, namely, Mel Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), Relative Spectral Trans-formed Perceptual Linear Prediction coefficients (RASTA-PLP), Amplitude Modulation Spectrogram (AMS), and Multi-Resolution Cochleagram (MRCG), on PESQ and STOI under varying noise conditions. Experimental outcomes underscore that MRCG consistently emerges as the optimal and most stable feature for STOI, while the feature yielding the highest PESQ score exhibits intricate correlations with the background noise type, SNR level, and noise compatibility with the speech signal. Consequently, we propose an SE methodology founded on quality assessment and feature selection, facilitating the adaptive selection of optimal features tailored to distinct background noise scenarios, thereby always maintain the highest caliber enhancement effect with regard to PESQ metrics.

Bidirectional EMD-RLS: Performance analysis for denoising in speech signal

Empirical Mode Decomposition (EMD) is an alternative decomposition method that allows the analysis of non-stationary and non-linear signals, decomposing them into components called Intrinsic Mode Functions (IMFs). In this paper, we introduce the bidirectional combination between EMD and the Recursive Least Squares (RLS) adaptive filter and compare it to Least Mean Square (LMS), EMD-LMS and RLS methods. The hybrid algorithm relies on the determination of vertical (IMFs indexes) and horizontal (temporal indexes) weighting coefficients. We evaluated the effectiveness of the methods by filtering sets of speech signals artificially corrupted by white Gaussian noise. To find the method that provides the greatest noise reduction and the best speech quality, we took into account the Signal-to-Noise Ratio and the Perceptual Evaluation of Speech Quality (PESQ) score. The analyzed cases showed that EMD-RLS can provide results as good as the other methods, but with a shorter learning curve than LMS and EMD-LMS, despite the higher computational cost. For a single training iteration, the methods presented PESQ scores on average equivalent to each other. However, they showed a significant difference in performance regarding noise reduction, with the best result obtained by RLS, followed by EMD-RLS, EMD-LMS and LMS.

Deep Learning-Based Speech Enhancement of an Extrinsic Fabry-Perot Interferometric Fiber Acoustic Sensor System.

To achieve high-quality voice communication technology without noise interference in flammable, explosive and strong electromagnetic environments, the speech enhancement technology of a fiber-optic external Fabry-Perot interferometric (EFPI) acoustic sensor based on deep learning is studied in this paper. The combination of a complex-valued convolutional neural network and a long short-term memory (CV-CNN-LSTM) model is proposed for speech enhancement in the EFPI acoustic sensing system. Moreover, the 3 × 3 coupler algorithm is used to demodulate voice signals. Then, the short-time Fourier transform (STFT) spectrogram features of voice signals are divided into a training set and a test set. The training set is input into the established CV-CNN-LSTM model for model training, and the test set is input into the trained model for testing. The experimental findings reveal that the proposed CV-CNN-LSTM model demonstrates exceptional speech enhancement performance, boasting an average Perceptual Evaluation of Speech Quality (PESQ) score of 3.148. In comparison to the CV-CNN and CV-LSTM models, this innovative model achieves a remarkable PESQ score improvement of 9.7% and 11.4%, respectively. Furthermore, the average Short-Time Objective Intelligibility (STOI) score witnesses significant enhancements of 4.04 and 2.83 when contrasted with the CV-CNN and CV-LSTM models, respectively.

Speech Enhancement Algorithm Based on a Convolutional Neural Network Reconstruction of the Temporal Envelope of Speech in Noisy Environments

Temporal modulation processing is a promising technique for improving the intelligibility and quality of speech in noise. We propose a speech enhancement algorithm that constructs the temporal envelope (TEV) in the time-frequency domain by means of an embedded convolutional neural network (CNN). To accomplish this, the input speech signals are divided into sixteen parallel frequency bands (subbands) with bandwidths approximating 1.5 times that of auditory filters. The corrupted TEVs in each subband are extracted and then fed to the 1-dimensional CNN (1-D CNN) model to restore the TEVs distorted by noise. The method is evaluated using 2,700 words from nine different talkers, which are mixed with speech-spectrum shaped random noise (SSN), and babble noise, at different signal-to-noise ratios. The Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) metrics are used to evaluate the performance of the 1-D CNN algorithm. Results suggest that the 1-D CNN model improves STOI scores on average by 27% and 34% for SSN and babble noise, respectively, and PESQ scores on average by 19% and 18%, respectively, compared to unprocessed speech. The 1-D CNN model is also shown to outperform a conventional TEV-based speech enhancement algorithm.

Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features

This study proposes a cross-domain multi-objective speech assessment model, called MOSA-Net, which can simultaneously estimate the speech quality, intelligibility, and distortion assessment scores of an input speech signal. MOSA-Net comprises a convolutional neural network and bidirectional long short-term memory architecture for representation extraction, and a multiplicative attention layer and a fully connected layer for each assessment metric prediction. Additionally, cross-domain features (spectral and time-domain features) and latent representations from self-supervised learned (SSL) models are used as inputs to combine rich acoustic information to obtain more accurate assessments. Experimental results show that in both seen and unseen noise environments, MOSA-Net can improve the linear correlation coefficient (LCC) scores in perceptual evaluation of speech quality (PESQ) prediction, compared to Quality-Net, an existing single-task model for PESQ prediction, and improve LCC scores in short-time objective intelligibility (STOI) prediction, compared to STOI-Net, an existing single-task model for STOI prediction. Moreover, MOSA-Net can be used as a pre-trained model to be effectively adapted to an assessment model for predicting subjective quality and intelligibility scores with a limited amount of training data. Experimental results show that MOSA-Net can improve LCC scores in mean opinion score (MOS) predictions, compared to MOS-SSL, a strong single-task model for MOS prediction. We further adopt the latent representations of MOSA-Net to guide the speech enhancement (SE) process and derive a quality-intelligibility (QI)-aware SE (QIA-SE) approach. Experimental results show that QIA-SE outperforms the baseline SE system with improved PESQ scores in both seen and unseen noise environments over a baseline SE model.

Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet

Speech enhancement employing deep neural networks (DNNs) for denoising is called deep noise suppression (DNS). The DNS trained with mean squared error (MSE) losses cannot guarantee good perceptual quality. Perceptual evaluation of speech quality (PESQ) is a widely used metric for evaluating speech quality. However, the original PESQ algorithm is non-differentiable, therefore, cannot directly be used as optimization criterion for gradient-based learning. In this work, we propose an end-to-end non-intrusive <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>PESQNet</b></monospace> DNN to estimate the PESQ scores of the enhanced speech signal. Thus, by providing a reference-free perceptual loss, it serves as a mediator towards the DNS training, allowing to maximize the PESQ score of the enhanced speech signal. We illustrate the potential of our proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>PESQNet</b></monospace> -mediated training on a strong baseline DNS. As further novelty, we propose to train the DNS and the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>PESQNet</b></monospace> alternatingly to keep the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>PESQNet</b></monospace> up-to-date and perform well specifically for the DNS under training. Detailed analysis shows that the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>PESQNet</b></monospace> mediation further increases the DNS performance by about 0.1 PESQ points on synthetic test data and by 0.03 DNSMOS points on real test data, compared to training with the MSE-based loss. Our proposed method outperforms the Interspeech 2021 DNS Challenge baseline by 0.2 PESQ points on synthetic test data and 0.1 DNSMOS points on real test data. Furthermore, it improves on the same DNS trained with an approximated differentiable PESQ loss by about 0.4 PESQ points on synthetic test data and 0.2 DNSMOS points on real test data.

Improved Speech Enhancement Considering Speech PSD Uncertainty

Speech enhancement based on statistical models has been studied for several decades. Recently, the speech enhancement adopting a speech power spectral density (PSD) uncertainty model has been proposed. This approach distinguishes the true speech PSD from its estimate and considers both as random variables. It incorporates a prior distribution of speech spectra and speech PSD estimators to derive the PSD uncertainty-aware counterpart to conventional clean speech estimators, which results in performance improvement. However, the speech PSD uncertainty model has not yet been adopted for parameter estimations such as <inline-formula><tex-math notation="LaTeX"><?TeX $\mathit{a posteriori}$?></tex-math></inline-formula> speech presence probability, noise PSD, and speech power spectra estimations in the speech enhancement framework. In this paper, we incorporate the speech PSD uncertainty model to all the components of the statistical model-based speech enhancement framework by deriving PSD uncertainty-aware counterparts to conventional parameter estimators. Specifically, we derive the <inline-formula><tex-math notation="LaTeX"><?TeX $\mathit{a posteriori}$?></tex-math></inline-formula> speech presence probability (SPP) where the likelihood function for each hypothesis is based on the speech PSD uncertainty. With this <inline-formula><tex-math notation="LaTeX"><?TeX $\mathit{a posteriori}$?></tex-math></inline-formula> SPP, a novel SPP-based noise PSD estimator is derived. Also, we derive the minimum mean-square error (MMSE) estimator for the power spectrum of the clean speech in the current frame under speech PSD uncertainty which is exploited to refine the speech PSD estimator. Finally, the refined speech PSD estimator is incorporated into the spectral gain function based on the speech PSD uncertainty model. The proposed approach showed improved noise PSD estimation performance in terms of the averaged logarithmic error distance, and improved speech enhancement performance in terms of the noise reduction, segmental signal-to-noise ratio, perceptual evaluation of speech quality (PESQ) scores and short-time objective intelligibility in our experiments. It also exhibited comparable performance with a real-time deep learning-based speech enhancement system in terms of the PESQ scores and composite measures for the VoiceBank-DEMAND dataset.

Differentiable Measures for Speech Spectral Modeling

Autoregressive models for the envelope of speech power spectral densities (PSDs) are refined by the self-supervised spectral learning machine (S3LM) provided with differentiable spectral objective functions, including the Itakura-Saito divergence (ISD), the Kullback-Leibler divergence (KLD), the reverse KLD (RKLD) and the log spectral distortion (LSD), which display more significant results. However, in order to assess the models more perceptually, a method is proposed based upon perturbations around perfect reconstruction analysis-synthesis configurations. In the cross-excitation analysis-synthesis assessment (CEASA) method, the residual signals generated by analysis filters of the spectral models are injected as excitation into the synthesis filters derived from the same and other models in order to be evaluated by the perceptual evaluation of speech quality (PESQ) and Itakura divergence (ID), which are averaged over a set of models obtained using the objective functions mentioned above. The results lead to a superior performance when the RKLD is used as the loss function for the estimation of the spectral models with the ISD ranking close behind. The focus of these divergences on the spectral peaks is argued and pointed as the most important factor for this behavior. Specifically, using the PESQ scores obtained with CEASA, the RKLD loss is found to improve the performance by 1.0%, 4.0% and 19.3% with respect to the open-loop analysis, the KLD and the LSD models, respectively, while the corresponding improvements for the ISD loss are 0.1%, 3.0% and 18.2%, and the RKLD models excel the ISD models by 1.0% on average. Even though the spectral measures alone are not able to unequivocally distinguish the better of the two, CEASA is shown to have enough sensitivity to distinguish their performances. In summary, the learning machine S3LM fits models for the short-term spectral envelope of speech and, for the evaluation of its performance under several differentiable loss functions, the CEASA assessment tool has been developed. In addition, CEASA may be used for other assessments connected with speech analysis and synthesis.

Components loss for neural networks in mask-based speech enhancement

Estimating time-frequency domain masks for single-channel speech enhancement using deep learning methods has recently become a popular research field with promising results. In this paper, we propose a novel components loss (CL) for the training of neural networks for mask-based speech enhancement. During the training process, the proposed CL offers separate control over preservation of the speech component quality, suppression of the noise component, and preservation of a naturally sounding residual noise component. We illustrate the potential of the proposed CL by evaluating a standard convolutional neural network (CNN) for mask-based speech enhancement. The new CL is compared to several baseline losses, comprising the conventional mean squared error (MSE) loss w.r.t. speech spectral amplitudes or w.r.t. an ideal-ratio mask, auditory-related loss functions, such as the perceptual evaluation of speech quality (PESQ) loss and the perceptual weighting filter loss, and also the recently proposed SNR loss with two masks. Detailed analysis suggests that the proposed CL obtains a better or at least a more balanced performance across all employed instrumental quality metrics, including SNR improvement, speech component quality, enhanced total speech quality, and particularly also delivers a natural sounding residual noise component. For unseen noise types, we excel even perceptually motivated losses by an about 0.2 points higher PESQ score. The recently proposed so-called SNR loss with two masks not only requires a network with more parameters due to the two decoder heads, but also falls behind on PESQ and POLQA and particularly w.r.t. residual noise quality. Note that the proposed CL shows significantly more 1st ranks among the evaluation metrics than any other baseline. It is easy to implement, and code is provided at https://github.com/ifnspaml/Components-Loss.

Noise reduction optimization of sound sensor based on a Conditional Generation Adversarial Network

To address the problems in the traditional speech signal noise elimination methods, such as the residual noise, poor real-time performance and narrow applications a new method is proposed to eliminate network voice noise based on deep learning of conditional generation adversarial network. In terms of the perceptual evaluation of speech quality (PESQ) and shorttime objective intelligibility measure (STOI) functions used as the loss function in the neural network, which were used as the loss function in the neural network, the flexibility of the whole network was optimized, and the training process of the model simplified. The experimental results indicate that, under the noisy environment, especially in a restaurant, the proposed noise reduction scheme improves the STOI score by 26.23% and PESQ score by 17.18%, respectively, compared with the traditional Wiener noise reduction algorithm. Therefore, the sound sensor’s noise reduction scheme through our approach has achieved a remarkable noise reduction effect, more useful information transmission, and stronger practicability.

A comparative study of sound sources separation by independent component analysis and binaural model

Humans’ auditory system can separate mixed sounds based on their sources easily. However, mimicking this ability by computer algorithm is not an easy task. Some approaches have been developed, particularly based on the statistical approach and binaural modeling. From statistical methods, independent component analysis (ICA) grows fast to mimics sound separation and localization by human auditory processing. On the other side, mathematical modeling to model binaural hearing has been built block by block. This paper is a comparative study of both approaches, a statistical method represented by FastICA and binaural modeling represented by the frequency domain binaural model. The task is to mimic how to binaural processing works to separate sound sources. The result of the comparison was given by the perceptual evaluation of speech quality (PESQ) and Itakura-Saito (IS) distortion measurement. PESQ scores ICA method obtains better performance than the binaural model while, in contrast, IS scores the binaural model better than ICA.

Multi-Task Learning U-Net for Single-Channel Speech Enhancement and Mask-Based Voice Activity Detection

In this paper, a multi-task learning U-shaped neural network (MTU-Net) is proposed and applied to single-channel speech enhancement (SE). The proposed MTU-based SE method estimates an ideal binary mask (IBM) or an ideal ratio mask (IRM) by extending the decoding network of a conventional U-Net to simultaneously model the speech and noise spectra as the target. The effectiveness of the proposed SE method was evaluated under both matched and mismatched noise conditions between training and testing by measuring the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). Consequently, the proposed SE method with IRM achieved a substantial improvement with higher average PESQ scores by 0.17, 0.52, and 0.40 than other state-of-the-art deep-learning-based methods, such as the deep recurrent neural network (DRNN), SE generative adversarial network (SEGAN), and conventional U-Net, respectively. In addition, the STOI scores of the proposed SE method are 0.07, 0.05, and 0.05 higher than those of the DRNN, SEGAN, and U-Net, respectively. Next, voice activity detection (VAD) is also proposed by using the IRM estimated by the proposed MTU-Net-based SE method, which is fundamentally an unsupervised method without any model training. Then, the performance of the proposed VAD method was compared with the performance of supervised learning-based methods using a deep neural network (DNN), a boosted DNN, and a long short-term memory (LSTM) network. Consequently, the proposed VAD methods show a slightly better performance than the three neural network-based methods under mismatched noise conditions.

Learning With Learned Loss Function: Speech Enhancement With Quality-Net to Improve Perceptual Evaluation of Speech Quality

Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently. The main reason is that the conventional mean squared error (MSE) loss cannot represent auditory perception well. One of the typical hu-man-perception-related metrics, which is the perceptual evaluation of speech quality (PESQ), has been proven to provide a high correlation to the quality scores rated by humans. Owing to its complex and non-differentiable properties, however, the PESQ function may not be used to optimize speech enhancement models directly. In this study, we propose optimizing the enhancement model with an approximated PESQ function, which is differentiable and learned from the training data. The experimental results show that the learned surrogate function can guide the enhancement model to further boost the PESQ score (in-crease of 0.18 points compared to the results trained with MSE loss) and maintain the speech intelligibility.

Sparsity-based phase spectrum compensation for single-channel speech source separation

This paper proposes a sparsity-based phase spectrum compensation (SPSC) function to improve the quality of reconstructed signals by using magnitude-based single-channel speech source separation. While conventional approaches to the reconstruction of separated sources use the input phase spectrum, the proposed SPSC function modifies each source's phase spectrum using the estimated magnitude spectra of multiple sources and their spectro-temporal sparsity. In particular, the spectro-temporal sparsity is estimated from the signal-to-interference ratio between the magnitude spectrum of a source to be separated and those of the other sources, including the background noise. The proposed SPSC function is first embedded into the reconstruction stage of magnitude-based speech source separation methods that employ deep recurrent neural network (DRNN) and sparse nonnegative matrix factorization (SNMF), respectively. Then, speech denoising is performed under four different noise conditions with signal-to-noise ratios (SNRs) in the range of 0–15 dB. Both objective and subjective tests show that both DRNN and SNMF-based speech separation methods employing the proposed SPSC function substantially outperform those employing the conventional PSC function for the speech denoising task. Moreover, the DRNN combined with the proposed SPSC function offers higher average perceptual evaluation of speech quality (PESQ) scores for speech denoising than SEGAN, which is based on a very large end-to-end neural networks model. Next, the proposed SPSC method is also applied on the reconstruction stage of a deep clustering-based speech separation method to examine its contribution on a multi-talker speech separation task, where spoken utterances of males and females are mixed at −3, 0, and 3 dB SNR. It is shown from the experiment that the proposed SPSC function further improves SDR and PESQ scores of the magnitude spectra processed by the deep clustering-based single-channel speech separation method.

Speech enhancement using adaptive thresholding based on gamma distribution of Teager energy operated intrinsic mode functions

This paper introduces a new speech enhancement algorithm based on the adaptive threshold of intrinsic mode functions (IMFs) of noisy signal frames extracted by empirical mode decomposition. Adaptive threshold values are estimated by using the gamma statistical model of Teager energy operated IMFs of noisy speech and estimated noise based on symmetric Kullback–Leibler divergence. The enhanced speech signal is obtained by a semisoft thresholding function, which is utilized by threshold IMF coefficients of noisy speech. The method is tested on the NOIZEUS speech database and the proposed method is compared with wavelet-shrinkage and EMD-shrinkage methods in terms of segmental SNR improvement (SegSNR), weighted spectral slope (WSS), and perceptual evaluation of speech quality (PESQ). Experimental results show that the proposed method provides a higher SegSNR improvement in dB, lower WSS distance, and higher PESQ scores than wavelet-shrinkage and EMD-shrinkage methods. The proposed method shows better performance than traditional threshold-based speech enhancement approaches from high to low SNR levels.

Compressed Sensing Speech Signal Enhancement Research

&lt;span lang="EN-US"&gt;The proposed Compressive sensing method is a new alternative method&lt;/span&gt;&lt;span lang="EN-US"&gt;, it is&lt;/span&gt;&lt;span lang="EN-US"&gt; used to eliminate noise from the input signal&lt;/span&gt;&lt;span lang="EN-US"&gt;,&lt;/span&gt;&lt;span lang="EN-US"&gt; and the quality of the speech signal &lt;/span&gt;&lt;span lang="EN-US"&gt;is &lt;/span&gt;&lt;span lang="EN-US"&gt;enhance&lt;/span&gt;&lt;span lang="EN-US"&gt;d&lt;/span&gt;&lt;span lang="EN-US"&gt; with fewer samples&lt;/span&gt;&lt;span lang="EN-US"&gt;, thus it is&lt;/span&gt;&lt;span lang="EN-US"&gt; required for the reconstruction than needed in some of the methods like Nyquist sampling theorem. The basic idea is&lt;/span&gt;&lt;span lang="EN-US"&gt; that &lt;/span&gt;&lt;span lang="EN-US"&gt;the speech signals are sparse in nature&lt;/span&gt;&lt;span lang="EN-US"&gt;,&lt;/span&gt;&lt;span lang="EN-US"&gt; and most of the noise signals are non-sparse in nature, and Compressive &lt;/span&gt;&lt;span lang="EN-US"&gt;S&lt;/span&gt;&lt;span lang="EN-US"&gt;ensing&lt;/span&gt;&lt;span lang="EN-US"&gt;(&lt;/span&gt;&lt;span lang="EN-US"&gt;CS) eliminates the non-sparse components and &lt;/span&gt;&lt;span lang="EN-US"&gt;it &lt;/span&gt;&lt;span lang="EN-US"&gt;reconstructs only the sparse components of the input signal. Experimental results prove that the average segmental SNR (signal to noise ratio) and PESQ (perceptual evaluation of speech quality) scores are better in the compressed domain&lt;/span&gt;&lt;span lang="EN-US"&gt;．&lt;/span&gt;

Simulation and overall comparative evaluation of performance between different techniques for high band feature extraction based on artificial bandwidth extension of speech over proposed global system for mobile full rate narrow band coder

This paper addresses a novel approach to investigate, study and simulate computation of high band (HB) feature extraction based on linear predictive coding (LPC) and mel frequency cepstral coefficient (MFCC) techniques. Further, HB features are embedded into encoded bitstream of proposed global system for mobile (GSM) full rate (FR) 06.10 coder using joint source coding and data hiding before being transmitted to receiving terminal. At receiver, HB features are extracted to reproduce HB portion of speech and for the same different extension of excitation techniques are applied and their results evaluated in terms of quality (intelligibility and naturalness) and bandwidth. MATLAB based e-test bench is created for implementing the proposed artificial bandwidth extension (ABE) coder following series of simulations, that are carried out to discover and gain insight about the performance of it using subjective [mean opinion score (MOS)] and objective [perceptual evaluation of speech quality (PESQ)] analysis. The results obtained for both the analyses advocate that proposed ABE coder outperforms proposed GSM FR NB (legacy GSM FR) coder. While the fact remains that, compared to LPC based parameterizations over ABE coder, MFCC parameterization results in higher speech intelligibility which is evident from obtained slightly better PESQ and MOS scores.

Mean-Median based Noise Estimation Method using Spectral Subtraction for Speech Enhancement Technique

Background/Objectives: This article proposes a new noise estimation method which is based on mean and median statistical tools. In this article, the proposed method i.e., Mean-Median based noise estimation method have applied in the spectral subtraction method for speech enhancement technique. Methods/Statistical Analysis: The MATLAB/SIMULINK platform is used for simulating the simulation model of proposed technique. For performance evaluation of proposed noise estimation, Perceptual Evaluation of Speech Quality (PESQ) Score and Simulation time are chosen. Different noisy speech signal and clean speech signal have taken as input signal for proposed model and for finding PESQ score, respectively. Findings: The advantage of our proposed noise estimation method, it does not require signal to noise ratio, Voice Activity Detector, or histograms. This paper is carried comparison results with Modified Cascaded Median (MCM) based noise estimation method and also simulation time for different corrupted speech files. Application/Improvements: As per expected, proposed technique is given better speech quality signal (in PESQ scores). As compare to MCM based technique, it takes less simulation time and also no memory storage requirement.