Perceptual Evaluation Of Speech Quality Research Articles

Transmission and Decryption of the Audio Signal Masked with ECG by FDM Method

Today, the use of these methods as hybrids has provided the motivation to be a solution to important problems, since the existing methods are insufficient at some points in ensuring the security of personal data. In data security, the inability to decrypt and decrypt the signal to be encrypted retrospectively has always been the subject of research in terms of privacy. At this point, it was preferred to use the electrocardiography (ECG) signal, which is a signal that shows the vital signs of the human body and is also difficult to copy. In the study, firstly, the emulator circuit was obtained by using the mathematical model of the ECG signal. With this obtained signal, the audio signals are masked. The audio signal masked on the transmitter side and the signals providing synchronization were transmitted to the receiver side over a single channel using the frequency division multiplexing (FDM) method. Then, the sliding mode control (SMC) method was chosen for the synchronization of the ECG emulator circuits on the receiver and transmitter side. Histogram, spectral, mean square error (MSE), peak signal to noise ratio (PSNR), key space and key sensitivity, NSCR (number of sample change rate), UACI (unified average changing intensity) and PESQ (perceptual evaluation of speech quality) analyses were used to check the accuracy of the system. These analyses showed that the ECG encoding method has faster unit change, reduces synchronization time, minimizes losses and improves the security of the masked signal compared to other methods sent from two channels. Finally, use of an arrhythmia ECG signal for the synchronization signal on both the transmitter and receiver sides, the synchronization of this signal with the SMC method and the testing of a live audio recording in addition to the conversation, distinguishes the study from other existing studies and reveals its originality.

Automated Dual-Channel Speech Enhancement Using Adaptive Coherence Function with Optimised Discrete Wavelet Transform

Voice quality enhancement is a significant method for any speech communication model. Speech Enhancement (SE) and noise reduction approaches can significantly improve the perceptual voice quality of a hands-free communication system and increase the recognition rates of automatic speech recognition systems. Speech communications in real-world cases require high-performance enhancement techniques for addressing the distortions, which can corrupt the intelligibility and quality of the speech signal. Recent portable devices generally incorporate several microphones that can be easily used for improving signal quality. This paper plans to present a novel dual-channel SE model using the coherence function and heuristic concepts. The adaptive coherence function relates to the dual-microphone SE approach suitable for smartphones with primary and reference microphones. With this improved signal, the enhancement is performed by optimising denoising using Discrete Wavelet Transform (DWT) by Adaptive wind speed-based Deer Hunting Optimization Algorithm (AWS-DHOA). The considered objective function depends on the quality measure called Perceptual Evaluation of Speech Quality (PESQ) score. From the results, the RMSE of the proposed model using AWS-DHOA is 39.8%, 45.5%, 53.8% and 45.5% minimised than GWO-CFD, WOA-CFD, CSA-CFD, and RDA-CFD, respectively, on considering the babble noise. Finally, the comparative analysis confirmed that the proposed method improves speech quality and intelligibility by comparing diverse algorithms when different noise types corrupt the speech.

Research on Speech Enhancement Algorithm of Multiresolution Cochleagram Based on Skip Connection Deep Neural Network

The speech enhancement effect of traditional deep learning algorithms is not ideal under low signal-to-noise ratios (SNR). Skip connections-deep neural network (Skip-DNN) improves the traditional deep neural network (DNN) by adding skip connections between each layer of the neural network to solve the degradation problem of DNN. In this paper, the Multiresolution Cochleagram (MRCG) features in the gammachirp transform domain are denoised to obtain the improved MRCG (I-MRCG). The noise reduction method adopts the Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator (MMSE-STSA) and takes I-MRCG as the input feature and Skip-DNN as the training network to improve the speech enhancement effect of the model. This paper also proposes an improved source-to-distortion ratio (SDR) loss function. When the loss function uses the improved SDR, it will improve the performance of Skip-DNN speech enhancement model. The experiments in this paper are performed on the Edinburgh dataset. When using I-MRCG as the input feature of Skip-DNN, the average perceptual evaluation of speech quality (PESQ) is 2.9137, and the average short-time objective intelligibility (STOI) is 0.8515. Compared with MRCG as Skip-DNN input features, the improvements are 0.91% and 0.71%, respectively. When the improved SDR is used as the loss function of the speech model, the average PESQ is 2.9699 and the average STOI is 0.8547. Compared with other loss functions, the improved SDR has a better enhancement effect when used as the loss function of the speech enhancement model.

Gammatone Filter Bank-Deep Neural Network-based Monaural speech enhancement for unseen conditions

Speech signal enhancement achieves high-level performance in recent years using deep learning techniques. However, the deep learning technique in the speech enhancement algorithm degrades the performance of speech, particularly for unseen noises, unseen speakers and moreover, deep learning models are limited to the small number of speakers. Hence, we propose a Gammatone filterbank (GTFB) – simple deep neural network (SDNN) based speech enhancement algorithm to improve the quality of speech for three different unseen conditions. The use of GTFB gives a finer resolution in low-frequency regions of speech, and the SDNN model extracts a noisy GTFB frame as input and maps it to a clean speech GTFB frame. The experimental results are measured objectively using signal-noise-ratio, perceptual evaluation of speech quality, short time objective intelligibility, and subjectively using mean opinion score. The experimental results are carried out using a variety of training and testing models. The performance results show that the proposed GTFB-SDNN are robust to a variety of test situations and outperform existing methods.

Dual-path transformer-based network with equalization-generation components prediction for flexible vibrational sensor speech enhancement in the time domain.

The flexible vibrational sensor (FVS) has the potential to become a popular wearable communication device because of its natural noise shielding characteristics and soft materials. However, FVS speech faces a severe loss of frequency components. To improve speech quality, a time-domain neural network model based on the dual-path transformer combined with equalization-generation components prediction (DPT-EGNet) is proposed. More specifically, the DPT-EGNet consists of five modules, namely the pre-processing module, dual-path transformer module, equalization module, generation module, and post-processing module. The dual-path transformer module is leveraged to extract the local and global contextual relationship of long-term speech sequences, which is extremely beneficial for inferring the missing components. The equalization and generation modules are designed according to the characteristics of FVS speech, which further improve the speech quality by simulating the inversion process of the speech distortion. The experimental results demonstrate that the proposed model effectively improves the quality of FVS speech; the average perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and composite measure for overall speech quality (COVL) scores of three males and three females are relatively increased by 64.19%, 29.63%, and 101.37%, which is superior to other baseline models developed in different domains. The proposed model also has significantly lower complexity than the others.

A Modified Ultraspherical Window and Its Application for Speech Enhancement

In this paper an improved ultraspherical window is developed for designing quadrature mirror filters banks (QMF) with the help of a real coded genetic algorithm (RCGA). In fact, the ultraspherical window is modified by adding a parameter (α) which to improve the spectral parameters. Then, RCGA is used to find optimal values of the adjustment parameters, the side-lobes ratio of ultraspherical window and the cut-off frequency of the low-pass prototype filter. This latter, is used to derive all the filters of QMF banks. When the developed QMF banks are exploited for speech enhancement algorithm based on wavelets techniques it gives good performances in terms of Perceptual Evaluation of Speech Quality (PESQ) and Signal to Noise Ratio (SNR). In addition to this, when compared to Dolph-Chebyshev, Kaiser and the original Ultraspherical windows the modified Ultraspherical window gives better performance referred to the obtained simulation results.

Performance analysis of low complexity fully connected neural networks for monaural speech enhancement

We compare the run-time complexity of recent deep neural network (DNN) and non-DNN based monaural speech enhancement algorithms. Specifically, we consider fully connected, convolutional, and genetic-algorithm based DNNs and compare their performance to the image analysis technique, which is non-DNN based. It is demonstrated that for the same speech enhancement performance, a simple fully connected DNN has the lowest run-time computational complexity in terms of floating-point operations and execution time on a standard laptop. The objective indices used for the evaluation of the speech enhancement performance are the perceptual evaluation of speech quality and short-time objective intelligibility measures. In addition, the subjective intelligibility measures involved in the experiment are the modified rhyme test and the mean opinion score. Both stationary and non-stationary noise in addition to interfering speech is considered.

Ultrasonic Doppler Based Silent Speech Interface Using Perceptual Distance

Moderate performance in terms of intelligibility and naturalness can be obtained using previously established silent speech interface (SSI) methods. Nevertheless, a common problem associated with SSI has involved deficiencies in estimating the spectrum details, which results in synthesized speech signals that are rough, harsh, and unclear. In this study, harmonic enhancement (HE), was used during postprocessing to alleviate this problem by emphasizing the spectral fine structure of speech signals. To improve the subjective quality of synthesized speech, the difference between synthesized and actual speech was established by calculating the distance in the perceptual domains instead of using the conventional mean square error (MSE). Two deep neural networks (DNNs) were employed to separately estimate the speech spectra and the filter coefficients of HE, connected in a cascading manner. The DNNs were trained to incrementally and iteratively minimize both the MSE and the perceptual distance (PD). A feasibility test showed that the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility measure (STOI) were improved by 17.8 and 2.9%, respectively, compared with previous methods. Subjective listening tests revealed that the proposed method yielded perceptually preferred results compared with that of the conventional MSE-based method.

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.

CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application

This study presents a deep learning-based speech signal-processing mobile application known as CITISEN. The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC), allowing CITISEN to be used as a platform for utilizing and evaluating SE models and flexibly extend the models to address various noise environments and users. For SE, a pretrained SE model downloaded from the cloud server is used to effectively reduce noise components from instant or saved recordings provided by users. For encountering unseen noise or speaker environments, the MA function is applied to promote CITISEN. A few audio samples recording on a noisy environment are uploaded and used to adapt the pretrained SE model on the server. Finally, for BNC, CITISEN first removes the background noises through an SE model and then mixes the processed speech with new background noise. The novel BNC function can evaluate SE performance under specific conditions, cover people's tracks, and provide entertainment. The experimental results confirmed the effectiveness of SE, MA, and BNC functions. Compared with the noisy speech signals, the enhanced speech signals achieved about 6\% and 33\% of improvements, respectively, in terms of short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ). With MA, the STOI and PESQ could be further improved by approximately 6\% and 11\%, respectively. Finally, the BNC experiment results indicated that the speech signals converted from noisy and silent backgrounds have a close scene identification accuracy and similar embeddings in an acoustic scene classification model. Therefore, the proposed BNC can effectively convert the background noise of a speech signal and be a data augmentation method when clean speech signals are unavailable.

SNR Classification Based Multi-Estimator IRM Speech Enhancement Algorithm

Deep neural network(DNN)-based ideal ratio mask(IRM) estimation methods are often adopted in speech enhancement tasks. In the previous work, IRM estimation was usually realized by a single DNN-based IRM estimator without considering the SNR levels, which had a limited performance in real applications. Therefore, a two stage speech enhancement method is proposed in this paper. Firstly, a DNN-based SNR classifier is employed to classify the speech frames into three classes according to different SNR thresholds. Secondly, three corresponding DNN based IRM estimators related to the three SNR classes are trained respectively, from which the amplitude spectrum is corrected. Finally, speech enhancement is realized by doing IDFT to the corrected speech spectrum combined with the phase information of noisy speech. Experiment results show that the algorithm proposed in this paper has better performances in the evaluation of short time objective intelligibility(STOI), perceptual evaluation of speech quality(PESQ) and segmental signal-to-noise ratio improvement(SSNRI) scores.

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.

A Compact CNN-Based Speech Enhancement With Adaptive Filter Design Using Gabor Function and Region-Aware Convolution

Speech enhancement (SE) is used in many applications, such as hearing devices, to improve speech intelligibility and quality. Convolutional neural network-based (CNN-based) SE algorithms in literature often employ generic convolutional filters that are not optimized for SE applications. This paper presents a CNN-based SE algorithm with an adaptive filter design (named ‘CNN-AFD’) using Gabor function and region-aware convolution. The proposed algorithm incorporates fixed Gabor functions into convolutional filters to model human auditory processing for improved denoising performance. The feature maps obtained from the Gabor-incorporated convolutional layers serve as learnable guided masks (tuned at backpropagation) for generating adaptive custom region-aware filters. The custom filters extract features from speech regions (i.e., ‘region-aware’) while maintaining translation-invariance. To reduce the high cost of inference of the CNN, skip convolution and activation analysis-wise pruning are explored. Employing skip convolution allowed the training time per epoch to be reduced by close to 40%. Pruning of neurons with high numbers of zero activations complements skip convolution and significantly reduces model parameters by more than 30%. The proposed CNN-AFD outperformed all four CNN-based SE baseline algorithms (i.e., a CNN-based SE employing generic filters, a CNN-based SE without region-aware convolution, a CNN-based SE trained with complex spectrograms and a CNN-based SE processing in the time-domain) with an average of 0.95, 1.82 and 0.82 in short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ) and logarithmic spectral distance (LSD) scores, respectively, when tasked to denoise speech contaminated with NOISEX-92 noises at -5, 0 and 5 dB signal-to-noise ratios (SNRs).

Speech Enhancement in Wireless Communication System Using Hybrid Spectral-Kalman Filter

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

Multi-Channel Speech Enhancement using a Minimum Variance Distortionless Response Beamformer based on Graph Convolutional Network

The Minimum Variance Distortionless Response (MVDR) beamforming algorithm is frequently utilized to extract speech and noise from noisy signals captured from multiple microphones. A frequency-time mask should be employed to compute the Power Spectral Density (PSD) matrices of the noise and the speech signal of interest to obtain the optimal weights for the beamformer. Deep Neural Networks (DNNs) are widely used for estimating time-frequency masks. This paper adopts a novel method using Graph Convolutional Networks (GCNs) to learn spatial correlations among the different channels. GCNs are integrated into the embedding space of a U-Net architecture to estimate a Complex Ideal Ratio Mask (cIRM). We use the cIRM in an MVDR beamformer to further improve the enhancement system. We simulate room acoustics data to experiment extensively with our approach using different types of the microphone array. Results indicate the superiority of our approach when compared to current state-of-the-art methods. The metrics obtained by the proposed method are significantly improved, except the Scale-Invariant Source-to-Distortion Ratio (SI-SDR) score. The Perceptual Evaluation of Speech Quality (PESQ) score shows a noticeable improvement over the baseline models (i.e., 2.207 vs. 2.104 and 2.076). Our implementation of the proposed method can be found in the following link: https://github.com/3i-hust-asr/gnn-mvdr-final.

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.

94 GHz Asymmetric Antenna Radar for Speech Signal Detection and Enhancement via Variational Mode Decomposition and Improved Threshold Strategy

To further improve the detection distance and sensitivity of bio-radar, a 94 GHz asymmetric antenna radar sensor is employed to detect speech signal. However, the radar speech is often mixed with various noise, which will seriously affect the quality and intelligibility of the speech signal. Therefore, a novel method based on variational mode decomposition (VMD) and improved threshold strategy (ITS) is proposed in this paper for improving the quality and intelligibility of the radar speech. VMD is a novel adaptive decomposition method, which overcomes the problem of mode aliasing and end effect in empirical mode decomposition (EMD). ITS can overcome the limitation of traditional wavelet threshold and achieve the best compromise between speech intelligibility and noise reduction. Firstly, EMD is applied to determine the number of decomposition level, and then radar speech is decomposed into several limited bandwidth intrinsic mode functions by VMD. Secondly, ITS is employed to remove noise from useful modes which are determined by Pearson correlation coefficient (PCC). The performance of the proposed method is evaluated by perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI) and composite measures (CMs). The experimental results show that the radar sensor can detect long distance speech signal and the proposed method can effectively improve the quality and intelligibility of the radar speech signal. Due to the good performance, the proposed method will provide a promising alternative for various applications related to radar speech and traditional microphone speech signal enhancement.

A study of Voice-over-Internet Protocol quality metrics

Since the pandemic began, institutions have been implementing work/study-from-home. They mostly use VoIP applications to do online meetings. This study is trying to discover what metrics are the most preferred to measure the quality of VoIP applications by reviewing 38 relevant studies. We found that the most preferred Quality of Experience (QoE) metrics are Mean Opinion Score (MOS), Perceptual Evaluation of Speech Quality (PESQ), and Peak Signal-to-noise Ratio (PSNR). While the most preferred Quality of Service (QoS) metrics are jitter, bandwidth, packet loss, and throughput. We also found that there are not many new studies done on evaluating SIP quality.

Enhancing LSB Using Binary Message Size Encoding for High Capacity, Transparent and Secure Audio Steganography–An Innovative Approach

We propose a novel LSB-BMSE method that enhances LSB audio steganography. It uses an innovative mechanism, Binaries of Message Size Encoding (BMSE), to embed a secret message after hiding its size in random samples. First, the secret message is compressed using Huffman coding, then encrypted by AES-128. The audio cover is split into number of blocks depending on secret message size. A secure key <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{BMSE}$ </tex-math></inline-formula> , output from the BMSE mechanism, is used to embed the secret message in random blocks and bytes adaptively according to its size. It is implemented using MATLAB and standard parameters: Perceptual Evaluation of Speech Quality and NIST Statistical Test Suite were used to measure imperceptibility between cover & stego audios and randomness of BMSE mechanism respectively. The fidelity was tested using Mean Square Error, Peak Signal to Noise Ratio and Signal-to-Noise Ratio. Comprehensive experiments on widely used metrics demonstrate that LSB-BMSE significantly surpasses existing methods in terms of hiding capacity and imperceptibility. Moreover, LSB-BMSE shows resistance to brute force attacks and statistical analysis. Although it was robust towards re-sampling attacks, nevertheless was not vigorous towards noise nor LSB attacks. The key <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{BMSE}$ </tex-math></inline-formula> complies with Kerckhoff’s principle and exhibits avalanche criteria. The tested proposed LSB-BMSE proclaimed its effectiveness.

Comparison of Speech Quality and Intelligibility Assessments in University Classrooms

Estimates of speech quality and intelligibility for three university classrooms of small, medium and large sizes are presented. The quality and intelligibility of speech were assessed by objective methods using binaural room impulse responses, measured at 5-6 points of the premises. The measures of speech quality were log-spectral distortion (LSD), bark spectral distortion (BSD) and perceptual evaluation of speech quality (PESQ), and the objective measure of speech intelligibility was the speech transmission index (STI). Among the quality measures considered, only BSD is shown to be highly correlated with STI measures for all three classrooms. In this case, correlation coefficient R varies from minus 0.6 for a small room to minus 0.98 for a large room. The close relationship between PESQ and STI is observed only in the case of a large classroom (R = 0.96-0.99), and the LSD measure was found to be uncorrelated with STI for premises of all sizes. The obtained results can serve as a justification for the use of BSD instead of STI, and vice versa, in the acoustic examination of classrooms of different sizes.