Evaluation of digital watermarking on subjective speech quality

New methods of securing the distribution of audio content have been widely deployed in the last twenty years. Their impact on perceptive quality has, however, only been seldomly the subject of recent extensive research. We review digital speech watermarking state of the art and provide subjective testing of watermarked speech samples. Latest speech watermarking techniques are listed, with their specifics and potential for further development. Their current and possible applications are evaluated. Open-source software designed to embed watermarking patterns in audio files is used to produce a set of samples that satisfies the requirements of modern speech-quality subjective assessments. The patchwork algorithm that is coded in the application is mainly considered in this analysis. Different watermark robustness levels are used, which allow determining the threshold of detection to human listeners. The subjective listening tests are conducted following ITU-T P.800 Recommendation, which precisely defines the conditions and requirements for subjective testing. Further analysis tries to determine the effects of noise and various disturbances on watermarked speech’s perceived quality. A threshold of intelligibility is estimated to allow further openings on speech compression techniques with watermarking. The impact of language or social background is evaluated through an additional experiment involving two groups of listeners. Results show significant robustness of the watermarking implementation, retaining both a reasonable net subjective audio quality and security attributes, despite mild levels of distortion and noise. Extended experiments with Chinese listeners open the door to formulate a hypothesis on perception variations with geographical and social backgrounds.

Subjective Quality Assessment for Cloud Gaming

Using subjective testing, we study the effect of the network parameters, delay and packet loss ratio, on the QoE of cloud gaming. We studied three different games, selected based on genre, popularity, content complexity and pace, and tested them in a controlled network environment, using a novel emulator to create realistic lognormal delay distributions instead of relying on a static mean delay, as used previously; we also used Parsec as a good representative of the state of the art. We captured user ratings on an ordinal Absolute Category Rating scale for three quality dimensions: Video QoE, Game-Playability QoE, and Overall QoE. We show that Mean Opinion Scores (MOS) for the game with the highest levels of content complexity and pace are most severely affected by network impairments. We also show that the QoE of interactive cloud applications rely more on the game playability than the video quality of the game. Unlike earlier studies, the differences in MOS are validated using the distributions of the underlying dimensions. A Wilcoxon Signed-Rank test showed that the distributions of Video QoE and Game Playability QoE are not significantly different.

Virtual bass preprocessing and carrier frequency optimization for the parametric array loudspeaker

The parametric array loudspeaker (PAL) is a novel type of loudspeaker that can project a directional sound beam. It is usually used in creating personal sound zone and projecting private messages to a targeted audience. However, the PAL has a very poor low-frequency response due to the inherent nonlinear acoustic principle generating sound from ultrasound in air. A psychoacoustic signal processing method known as the virtual bass (VB) has been proved to be an effective method to improve the bass quality of consumer electronics with miniature or flat loudspeaker unit. This paper proposes the VB processing based on the phase vocoder (PV) for the bass enhancement of the PAL that adopts a vestigial sideband modulation method. The harmonics generated by the VB processing are presented in the partial sideband, while the audio input without the bass component is embedded in the full sideband. A measure, namely the in-band peak flatness, is thereafter proposed in this paper to determine the optimal carrier frequency, given a practical uneven frequency response of the ultrasonic transducer. The subjective testing results validate that the proposed VB processing together with the optimal carrier frequency can finally realize the improvement of bass sound quality of the PAL.

Content Dependent Representation Selection Model for Systems Based on MPEG DASH

A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available literature that focus on video segmentation are mostly proprietary, use a high amount of computational power, lack the methodology, model notation, information needed for reproduction, or do not consider the spatial and temporal activity of video sequences. This paper presents a new model for selecting optimal parameters and number of representations for video encoding and segmentation, based on a measure of the spatial and temporal activity of the video content. The model was developed for the H.264 encoder, using Structural Similarity Index Measure (SSIM) objective metrics as well as Spatial Information (SI) and Temporal Information (TI) as measures of video spatial and temporal activity. The methodology that we used to develop the mathematical model is also presented in detail so that it can be applied to adapt the mathematical model to another type of an encoder or a set of encoding parameters. The efficiency of the segmentation made by the proposed model was tested using the Basic Adaptation algorithm (BAA) and Segment Aware Rate Adaptation (SARA) algorithm as well as two different network scenarios. In comparison to the segmentation available in the relevant literature, the segmentation based on the proposed model obtains better SSIM values in 92% of cases and subjective testing showed that it achieves better results in 83.3% of cases.

Noise reduction in hearing aids using spectral subtraction techniques

This thesis is concerned with noise reduction in hearing aids. Hearing - impaired listeners and hearing - impaired users have great difficulty understanding speech in a noisy background. This problem has motivated the development and the use of noise reduction algorithms to improve the speech intelligibility in hearing aids. In this thesis, two noise reduction algorithms for single channel hearing instruments are presented, evaluated using objective and subjective tests. The first noise reduction algorithm, conventional Spectral Subtraction, is simulated using MATLAB 6.5, R13. The second noise reduction algorithm, Spectral Subtraction in wavelet domanin is introduced as well. This algorithm is implemented off line, and is compared with conventional Spectral Subtraction. A subjective evaluation demonstrates that the second algorithm has additional advantages in speech intelligibility, in poor listening conditions relative to conventional Spectral Subtraction. The subjective testing was performed with normal hearing listeners, at Ryerson University. The objective evaluation shows that the Spectral Subtraction in wavelet domain has improved Signal to Noise Ratio compared to conventional Spectral Subtraction.

Noise reduction in hearing aids using spectral subtraction techniques

This thesis is concerned with noise reduction in hearing aids. Hearing - impaired listeners and hearing - impaired users have great difficulty understanding speech in a noisy background. This problem has motivated the development and the use of noise reduction algorithms to improve the speech intelligibility in hearing aids. In this thesis, two noise reduction algorithms for single channel hearing instruments are presented, evaluated using objective and subjective tests. The first noise reduction algorithm, conventional Spectral Subtraction, is simulated using MATLAB 6.5, R13. The second noise reduction algorithm, Spectral Subtraction in wavelet domanin is introduced as well. This algorithm is implemented off line, and is compared with conventional Spectral Subtraction. A subjective evaluation demonstrates that the second algorithm has additional advantages in speech intelligibility, in poor listening conditions relative to conventional Spectral Subtraction. The subjective testing was performed with normal hearing listeners, at Ryerson University. The objective evaluation shows that the Spectral Subtraction in wavelet domain has improved Signal to Noise Ratio compared to conventional Spectral Subtraction.

Geographical Variations in Host Predisposition to COVID-19 Related Anosmia, Ageusia, and Neurological Syndromes

The novel coronavirus disease (COVID-19), has become the most critical global health challenge in recent history. With SARS-CoV-2 infection, there was an unexpectedly high and specific prevalence of olfactory and taste disorders (OTDs). These high rates of hyposmia and hypogeusia, initially reported as up to 89% in European case series, led to the global inclusion of loss of taste and/or smell as a distinctive feature of COVID-19. However, there is emerging evidence that there are striking differences in the rates of OTDs in East Asian countries where the disease first emerged, as compared to Western countries (15.8 vs. 60.9%, p-value < 0.01). This may be driven by either variations in SARS-CoV-2 subtypes presenting to different global populations or genotypic differences in hosts which alter the predisposition of these different populations to the neuroinvasiveness of SARS-CoV-2. We also found that rates of OTDs were significantly higher in objective testing for OTDs as compared to subjective testing (73.6 vs. 60.8%, p-value = 0.03), which is the methodology employed by most studies. Concurrently, it has also become evident that racial minorities across geographically disparate world populations suffer from disproportionately higher rates of COVID-19 infection and mortality. In this mini review, we aim to delineate and explore the varying rates of olfactory and taste disorders amongst COVID-19 patients, by focusing on their underlying geographical, testing, ethnic and socioeconomic differences. We examine the current literature for evidence of differences in the olfactory and gustatory manifestations of COVID-19 and discuss current pathophysiological hypotheses for such differences.

Instance Segmentation Based on Deep Convolutional Neural Networks and Transfer Learning for Unconstrained Psoriasis Skin Images

In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head is used to predict the classification information, bounding box information, and mask coefficients of objects. Some prototypes generated by Protonet are combined with mask coefficients to estimate the pixel-level shapes for objects. To achieve instance segmentation for unconstrained psoriasis images, YOLACT++ with a pretrained model is retrained via transfer learning. To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels are collected for testing. As for subjective testing, the psoriasis regions and normal skin areas can be located and classified well. The four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the Mean Average Precision (mAP) rates of the proposed scheme were at least 85.9% after cross validation. As for efficiency, the frames per second (FPS) rate of the proposed scheme reached up to 15. In addition, the F1_score and the execution speed of the proposed scheme were higher than those of the Mask Region-Based Convolutional Neural Networks (R-CNN)-based method. These results show that the proposed scheme based on YOLACT++ can not only detect psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme outperforms the Mask R-CNN-based method for unconstrained psoriasis images.

Investigation of Weighted Scales for Measuring Visual Fatigue in Screen Tasks

<p>The “screening” trend of modern society has been progressively increasing burden on human visual system, and visual fatigue problems are attracting growing attention. Nowadays, subjective testing is the most widely used measurement for visual fatigue; however, the low accuracy of subjective testing has been hindering its further development. Motivated by the idea of weighted scoring, this study investigated the effects of two weighted scales for measuring visual fatigue in screening tasks. Specifically, a questionnaire with 10 items collected from classic scales was performed with an eye-tracking testing in two typical screen visual fatigue experiments, i.e., searching and watching. Then the subjective scores were factor-analyzed into three subscales before attempting linear regressions, which set the dependent to two previously validated eye-tracking parameters, i.e., fixation frequency or saccade amplitude. Finally two weighted scales were obtained in assessing visual fatigue of varying levels, which demonstrated the potential to improve testing accuracy of visual fatigue with the calibration of objective measurement.</p>

Investigation of Weighted Scales for Measuring Visual Fatigue in Screen Tasks

<p>The “screening” trend of modern society has been progressively increasing burden on human visual system, and visual fatigue problems are attracting growing attention. Nowadays, subjective testing is the most widely used measurement for visual fatigue; however, the low accuracy of subjective testing has been hindering its further development. Motivated by the idea of weighted scoring, this study investigated the effects of two weighted scales for measuring visual fatigue in screening tasks. Specifically, a questionnaire with 10 items collected from classic scales was performed with an eye-tracking testing in two typical screen visual fatigue experiments, i.e., searching and watching. Then the subjective scores were factor-analyzed into three subscales before attempting linear regressions, which set the dependent to two previously validated eye-tracking parameters, i.e., fixation frequency or saccade amplitude. Finally two weighted scales were obtained in assessing visual fatigue of varying levels, which demonstrated the potential to improve testing accuracy of visual fatigue with the calibration of objective measurement.</p>