Parkinson’s disease (PD) detection from speech signals attracts significant attention from researchers due to the effectiveness of speech impairment symptoms in PD diagnosis in its early phase. The short-time Fourier transform (STFT)-based analysis suffers from fixed resolution. The discrete wavelet transform (DWT) provides multiresolution analysis. However, an analytic wavelet transform with a flexible time-frequency covering (AWTFTFC) is a generalized form of DWT with flexible selection of redundancy, dilation factor, and Q-factor. The Fourier-Bessel series expansion (FBSE)-based flexible analytic wavelet transform (FAWT) (FBSE-FAWT) is an improved version of AWTFTFC with the replacement of the Fourier-based spectrum by the FBSE-based spectrum to have better frequency resolution. In this article, FBSE-FAWT-based time-frequency representation (TFR) is used to detect PD from speech signals. In the FBSE-FAWT-based TFR method, the FBSE-FAWT technique has been used to obtain subband signals and Hilbert spectral analysis is applied to compute TFR of speech signals. Seven different pretrained networks, namely, AlexNet, GoogleNet, SqueezeNet, DenseNet-101, InceptionNet-v3, VGG-16, and ResNet-50 are trained using the obtained TFRs to detect PD. The proposed framework with ResNet-50 demonstrates the highest performance with an accuracy of 98.05%, an F1-score of 98.27%, a specificity of 98.61%, a precision of 98.93%, and a recall of 97.62%. The robustness of the proposed framework is verified in cross-task situations where the framework has been trained on spontaneous dialogue task and tested on read text task. Further, the proposed framework achieves better performance as compared with its variant, where the FBSE-FAWT-based TFR is replaced with a spectrogram, as well as compared with other existing methods in the literature.

Neurological injuries or neurodegenerative diseases can lead to dysarthria, a condition that impairs speech intelligibility. Accurate detection of dysarthria and its severity from speech signals are crucial for advancing smart healthcare solutions. This study presents an automated system for dysarthria detection and severity classification, using a Fourier-Bessel series expansion-based flexible time-frequency coverage wavelet transform (FBSE-FTFCWT) and an autoencoder. Initially, FBSE-FTFCWT decomposes the speech signal into 16 sub-band signals, which are used as an input in the form of tensor for autoencoders to generate latent representation. This latent representation is subsequently used for dysarthric speech and its severity level detection. The proposed framework outperformed the current state-of-the-art in classifying dysarthric and normal speech on the UA-speech dataset, achieving 3.28% higher accuracy. Additionally, for the dysarthric severity detection task using speech signals from the same dataset, it showed 3.1% improvement in accuracy over existing methods.

Block-based compressive sensing (BCS) has evolved as a promising method for smart devices with limited bandwidth and computing capabilities, striking a balance between image/video quality and transmission efficiency. Despite its advantages, BCS falls short in reducing bitrate compared with traditional acquisition systems, because it increases the number of bits per measurement, which leads to high storage and transmission costs. In this context, we propose a measurement predictive coding (MPC) along with the quantization method in integration with BCS named BCS-MPC; here, we have performed the quantization with bit shifts only instead of binary division. The proposed method reduces the number of bits per compressive sensing (CS) measurement as well as the transmission of the quantization step size. Furthermore, it reduces the latency and hardware resources. The proposed method improved on average +3.44 to +8.28 dB in PSNR over the current works. From the synthesis results, the proposed BCS-MPC method requires 26.11%, 18.89%, and 82.53% less area, power, and delay over the existing work. We have achieved a reduction in delay with bit-shift operations.

This ongoing work introduces a novel chaotic oscillator, termed the Jagged-shape oscillator, into the filtering domain. We have formulated the dynamics of the Jagged-shape oscillator and integrated it with a noisy measurement model, capturing the complex behavior of various real-time systems. To estimate the unmeasured internal states of this chaotic system, we employ the third-degree spherical-cubature rule-based Kalman filter (CKF). The CKF has demonstrated efficiency in tracking the states of the chaotic oscillatory system. The accuracy of the state estimation is validated through different scenarios that consider low to increased noise levels.

The aircraft wing rock system is chaotic and highly oscillatory in nature. This work formulates the oscillatory chaotic dynamics of the wing rock system and integrates it with a noisy measurement model. Subsequently, we introduce a cubature rule-based particle filter (CPF) to track the roll angle and roll velocity (system internal states). The CPF uses the third-degree spherical-radial rule for integral approximation. The desired state estimation information may be used to design a state-based control scheme to regulate the motion of the wing rock. The simulation results showcase the performance of the proposed method in the presence of uncertainties.

We present a new subjective and objective study on full high-definition (HD) stereoscopic (3D or S3D) video quality. In subjective study, we constructed an S3D video dataset with 12 pristine and 288 test videos, and the test videos are generated by applying the H.264 and H.265 compression, blur and frame freeze artifacts. We also propose a no reference (NR) objective video quality assessment (QA) algorithm that relies on measurements of the statistical dependencies between the motion and disparity subband coefficients of S3D videos. Inspired by the Generalized Gaussian Distribution (GGD) approach in liu2011statistical, we model the joint statistical dependencies between the motion and disparity components as following a Bivariate Generalized Gaussian Distribution (BGGD). We estimate the BGGD model parameters (α,β) and the coherence measure (Ψ) from the eigenvalues of the sample covariance matrix (M) of the BGGD. In turn, we model the BGGD parameters of pristine S3D videos using a Multivariate Gaussian (MVG) distribution. The likelihood of a test video's MVG model parameters coming from the pristine MVG model is computed and shown to play a key role in the overall quality estimation. We also estimate the global motion content of each video by averaging the SSIM scores between pairs of successive video frames. To estimate the test S3D video's spatial quality, we apply the popular 2D NR unsupervised NIQE image QA model on a frame-by-frame basis on both views. The overall quality of a test S3D video is finally computed by pooling the test S3D video's likelihood estimates, global motion strength and spatial quality scores. The proposed algorithm, which is 'completely blind' (requiring no reference videos or training on subjective scores) is called the Motion and Disparity based 3D video quality evaluator (MoDi3D). We show that MoDi3D delivers competitive performance over a wide variety of datasets including the IRCCYN dataset, the WaterlooIVC Phase I dataset, the LFOVIA dataset and our proposed LFOVIAS3DPh2 S3D video dataset.

We propose a supervised no-reference (NR) quality assessment algorithm for assessing the perceptual quality of natural stereoscopic (S3D) videos. We empirically model the joint statistics of motion and depth subband coefficients of an S3D video frame using a Bivaraite Generalized Gaussian Distribution (BGGD). We compute the BGGD model parameters (α, β) to estimate the statistical dependency strength and show the features are quality discriminative. We compute the popular 2D NR image quality assessment (IQA) model NIQE on a frame-by-frame basis for both views to estimate the spatial quality. The frame-level BGGD features and spatial features are consolidated and used with the corresponding S3D videos difference mean opinion score (DMOS) labels for supervised learning using support vector regression (SVR). The overall quality of an S3D video is computed by averaging the frame-level quality predictions of the constituent video frames. The proposed algorithm, dubbed Video QUality Evaluation using MOtion and DEpth Statistics (VQUEMODES) is shown to outperform the state-of-the-art methods when evaluated over the IRCCYN and LFOVIA S3D subjective quality assessment databases.

Due to recent advent of light field visualization, ac-quisition/creation, encoding, transmission, rendering and quality assessment of 3D light field content has gained momentum. In particular, large light field displays need content with large field of view, and with high spatial and angular quality. Accordingly, subjective and objective quality evaluation studies have been conducted to examine spatial, angular and spatio-angular aspects of light field visualization. Recently, the effect of various zooming levels of the displayed content, as well as regions of interest on Quality of Experience (QoE) has also been explored. However, there has been no systematic attempt to see how the features of the content itself affect the visualization quality. In this work, we attempt to examine the effects of some primitive features of the content on subjective QoE. The results are based on a subjective study conducted on a large light field display, offering virtually continuous horizontal parallax.

In this letter, we present a full-reference (FR) quality assessment algorithm to assess the perceptual quality of natural stereoscopic three-dimensional (S3-D) videos. Toward the end, we rely on an empirical model for the joint statistics of motion and depth subband coefficients of an S3-D video frame. Specifically, we use the recently proposed bivariate generalized Gaussian distribution (BGGD) model for the joint statistics. In this letter, we show that the coherence of the covariance matrix of the BGGD varies in proportion with the perceptual video quality. We compute the coherence scores from the eigenvalues of the covariance matrix to estimate the amount of directional dependency between the motion and depth components. To estimate the overall spatial quality score, we apply off-the-shelf 2-D FR image quality assessment metrics on a frame-by-frame basis on both the views and average the frame wise scores. Finally, we pool the coherence and spatial quality scores to derive the overall quality for the S3-D video. An evaluation of the proposed algorithm on the IRCCYN, WaterlooIVC Phase I, and LFOVIA S3D video databases demonstrates its robust performance. The proposed algorithm is called depth- and motion-based 3-D video quality evaluator.

We propose a full reference stereo video quality assessment algorithm for assessing the perceptual quality of natural stereo videos. We exploit the separable representation of motion and binocular disparity in the visual cortex and develop a four stage algorithm to measure the quality of a stereoscopic video called FLOSIM3D. First, we compute the temporal features by utilizing an existing 2D VQA metric which measures the temporal annoyance based on patch level statistics such as mean, variance and minimum eigen value and pools them with a frame categorization based non-linear pooling strategy. Second, a structure based 2D Image Quality Assessment (IQA) metric is used to compute the spatial quality of the frames. Next, the loss in depth cues is measured using a structure based metric. Finally, the features for each of the stereo views are pooled to obtain the final stereo video quality score. We demonstrate the state-of-the-art performance of the proposed metric on IRCCYN dataset involving H.264, JP2K compression artifacts.

Empirical studies of the joint statistics of luminance and disparity images (or wavelet coefficients) of natural stereoscopic scenes have resulted in two important findings: the marginal statistics are modelled well by the generalized Gaussian distribution (GGD) and there exists significant correlation between them. Inspired by these findings, we propose a full-reference image quality assessment algorithm dubbed STeReoscopic Image Quality Evaluator (STRIQE). We show that the parameters of the GGD fits of luminance wavelet coefficients along with correlation values form excellent features. Importantly, we demonstrate that the use of disparity information (via correlation) results in a consistent improvement in the performance of the algorithm. The performance of our algorithm is evaluated over popular datasets and shown to be competitive with the state-of-the-art full-reference algorithms. The efficacy of the algorithm is further highlighted by its near-linear relation with subjective scores, low root mean squared error (RMSE), and consistently good performance over both symmetric and asymmetric distortions.