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Purpose Method Results Conclusion WHAT THIS PAPER ADDS What is already known on this subject What this paper adds to existing knowledge What are the clinical implications of this work? This study was designed based on the need to verify the effectiveness of education in the vocology area concerning refining perceptual auditory skills. The purpose of the project described in this paper was to examine the effect of knowledge and skills training during a 2‐week voice disorders graduate course on auditory‐perceptual skills of pre‐professional speech‐language pathologists. The training effect was evaluated by comparing pre‐ and post‐course results of ratings of voice quality using auditory‐perceptual measures.A cohort of 24 graduate students in speech‐language pathology participated in this study. Each student rated a total of 24 voice samples of typical and disordered voice quality from a database using Consensus Auditory‐Perceptual Evaluation of Voice (CAPE‐V). The same voice samples were rated before and after the training, in randomised order. The speakers ranged in age from 35 to 80 years; 16 identified as females and 8 as males. A total of 16 samples were associated with various degrees of dysphonia of organic and functional aetiologies; the remaining 8 voice samples included in the study were classified as normal. The training involved different components including knowledge and auditory‐perceptual skills as part of a voice class offered as an intensive 2‐week course with daily blocks of 4 h sessions. Exploratory comparisons of results to objective outcomes were conducted through comparisons with computer‐generated indicators of the Cepstral Spectral Index of Dysphonia (CSID), a component of the Analysis of Dysphonia in Speech and Voice (ADSV) software from Pentax for capturing the CSID.There was a significant increase in ratings of roughness, breathiness, loudness, pitch and overall severity after the course. Furthermore, there was a significant increase in the intra‐ and interrater reliability post training. Statistically significant positive indications were found in correlations between mean perceptual ratings and the acoustic measure of CSID. Reliability of data was measured by using Pearson's correlations. Intrarater reliability resulted in values of ≥0.90 in all parameters. Similar findings were demonstrated by intraclass coefficients used to estimate interrater consistency: all values were >0.90 but in the Loudness parameter [r (22) = 0.60].Effectiveness of the training in improving the auditory perceptual skills of students was based on the increased reliability of ratings. Indications of successful implementation and outcomes on auditory‐perpetual training procedures in classroom environments will support the development of effective educational methods, which will in turn lead to increased efficacy of service delivery in voice disorders. The effects of training on perceptual assessment performance have been indicated in the literature. Past evidence has demonstrated that various types of training produce a significant impact on auditory perceptual skills. Although dedicated research has been advancing over the past decade, further aspects of auditory‐perceptual reliable assessment include queries about specific training approaches, optimal duration, and interval time for recalibration. While past research suggests that increased exposure in identifying voice characteristics impacts assessment skills, there are limited published data on the relationship of knowledge and skill in association with teaching and learning environments, particularly at the pre‐professional stage. Practical data from actual classroom application constitute an ideal scenario for inspecting effectiveness of graduate training in voice disorders involving acquiring knowledge and applied skills, including auditory‐perceptual voice evaluation. This study is an attempt to expand on previous literature, particularly by examining effects of auditory‐perceptual skills training in graduate students following teaching and learning in a systematic 2‐week voice disorders course. Outcomes indicative of successful training procedures in a classroom environment will support the development of effective educational methods to increase efficacy of service delivery in voice disorders. [ABSTRACT FROM AUTHOR]

Prism adaptation (PA) is both a visuomotor learning task and potential treatment for spatial neglect after stroke. While PA's aftereffects can improve neglect symptoms, therapeutic benefits vary across individuals, possibly due to differences in neglect subtypes. Neglect symptoms can be described along an information processing pathway, yielding perceptual (input) and premotor (output) neglect subtypes. There is some evidence that PA mainly benefits persons with premotor neglect. We investigated whether PA modulates the premotor stage of information processing by examining whether PA could induce a premotor bias in healthy adults. We measured perceptual and premotor biases using a speeded reach task that compares the initiation time of leftward and rightward reaches to lateralized targets from different hand start positions. Using a randomized mixed experimental design, 30 right-handed healthy adults completed this speeded reach task before and after either left-shifting (n = 15) or right-shifting (n = 15) PA. As hypothesized, left-shifting PA speeded initiation time specifically for reaches in the rightward direction, regardless of target location (p =.02, ηp2 =.18), suggesting that PA induced a premotor bias in the direction of the prism aftereffect. These findings have implications for PA's underlying mechanisms, which can inform visuomotor learning theories and PA's use as a treatment for spatial neglect. [ABSTRACT FROM AUTHOR]

Multimodal medical fusion images (MMFI) are formed by fusing medical images of two or more modalities with the aim of displaying as much valuable information as possible in a single image. However, due to the different strategies of various fusion algorithms, the quality of the generated fused images is uneven. Thus, an effective blind image quality assessment (BIQA) method is urgently required. The challenge of MMFI quality assessment is to enable the network to perceive the nuances between fused images of different qualities, and the key point for the success of BIQA is the availability of valid reference information. To this end, this work proposes a generative adversarial network (GAN) -guided nuance perceptual attention network (G2NPAN) to implement BIQA for MMFI. Specifically, we achieve the blind evaluation style via the design of a GAN and develop a Unique FeatureWarehouse module to learn the effective features of fused images from the pixel level. The redesigned loss function guides the network to perceive the image quality. In the end, the class activation mapping supervised quality assessment network is employed to obtain the MMFI quality score. Extensive experiments and validation have been conducted in a database of medical fusion images, and the proposed method is superior to the state-of-the-art BIQA method. [ABSTRACT FROM AUTHOR]

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The Coronavirus Disease 2019 (COVID-19) is an ongoing global pandemic and wearing face mask is recommended across the globe to break the transmission chain of infection. The masks available in the market are of different types and materials and tend to alter the voice characteristics of the speaker. This can therefore impair optimal communication and the present study is a systematic review exploring the effect of various masks on voice production parameters. Systematic review. The titles and abstracts screening was carried out for the inclusion of articles using eight electronic databases spanning the period from 1st January 2020 to 30th April 2021. 10 articles (8 published & 2 in pre-print) that met the inclusion criteria were considered for this systematic review and the pooled age range was 18 –69 years. Three primary studies from the USA, 2 each from Australia & Italy, one each from Brazil, China, and Germany were found to have investigated the influence of wearing N95, KN95, surgical and fabric masks on voice related measures. The users significantly reported vocal fatigue, discomfort, and also perceived voice problems. Attenuation of speech sound amplitude was highest for the transparent mask followed by cloth mask, N95, KN95, and surgical mask. The World Health Organization (WHO) has been repeatedly endorsing the need to use a face mask in the current COVID-19 pandemic. However, for an unintruded voice production, the surgical mask is recommended for everyone, including healthcare professionals when they are not in close contact with patients, and not involved in aerosol-generating procedures. For teachers, doing direct teaching (offline classes), 'surgical mask' can reduce the vocal load of teachers, smoothen the teacher-student interaction and thereby facilitate better learning by the students. Additionally, it would be useful to protect oneself from the risk of developing voice problems by following standard vocal healthcare tips. [ABSTRACT FROM AUTHOR]

Although differential rating of perceived exertion (RPE) scales have been supported in quantifying internal load across various sports, their application in basketball remains to be comprehensively investigated. Consequently, we aimed to: (1) quantify and compare session- and weekly-RPE loads using global and differential RPE scales; and (2) compare session-RPE load between individual sessions across the week using each scale in basketball players. Ten semiprofessional, male players reported RPE using global and differential (respiratory and muscular) scales following each training session and game during the in-season. RPE was multiplied by session duration to derive session-RPE load, which were summed to determine weekly-RPE load. Weekly-RPE load was higher using global (P = 0.003, η2 = 0.343, large) and muscular (P = 0.004, η2 = 0.209, large) scales than the respiratory scale. Likewise, session-RPE load was higher using global (P = 0.049, η2 = 0.314, large) and muscular (P = 0.054, η2 = 0.298, large) scales than the respiratory scale only in games, with differences between scales during other sessions being trivial-to-medium (P >0.05). Across all scales, higher session-RPE loads were apparent in the second training session than all other sessions in the week (P <0.05, η2 = 0.105-0.561, medium-to-large), and during games than the first training session (P <0.001, η2 = 0.202-0.223, large). While session-to-session load changes were similarly detected across scales, the greater weekly and game muscular loads than respiratory loads support the potential for differential RPE scales to provide more detailed internal load data in basketball settings. [ABSTRACT FROM AUTHOR]

Understanding and optimizing the complex action of dynamic soccer players is vital for various applications in sports analytics and training. This study focuses on integrating multi-channel perceptual visual attributes to describe images of soccer games, which often include complex spatial arrangements. Central to our approach is the development of a deep multi-layer architecture tailored to precisely analyze soccer players’ movements, which are subsequently optimized by a probabilistic model. We utilize the BING objectness metric to efficiently locate representative patches within these soccer-relevant sceneries, such as players, the ball, or goal areasąłacross different scales. Following the localization process, both multi-task and manifold-guided mathematical models are formulated to select visual features from these key patches adaptively. To emulate the observational skills of soccer coaches and players in recognizing crucial moments of play, we introduce a technique known as locality-preserved and interactive active detection (LIAD). This method systematically generates gaze dynamic sequence (GDS) for each soccer scene, enhancing our understanding of soccer tactical formations and player positioning. The advantages of LIAD include its ability to: 1) preserve the local context of diverse game situations effectively, and 2) engage system designer in an active selection process driven by interactive feedback. By implementing LIAD, we methodically construct a GDS for each soccer scene and compute its deep attributes, which are then integrated into a probabilistic Gaussian mixture model (GMM) for optimize each soccer scenery. Our methodology’s efficacy is confirmed through comprehensive empirical studies, demonstrating its effectiveness in soccer training education. Notably, our derived GDSs have shown exceptional discriminative capability within our collected data set of educational soccer images, providing valuable insights for training and tactical analysis.

Deciphering the complex semantics within varied dancing sceneries is crucial for a multitude of AI endeavors. It can facilitate applications like dancing action optimization and dancing education. In our research, we propose a sophisticated approach to discerning multi-faceted perceptual visual features for accurately identifying dancing scenic imagery with intricate spatial designs. Our work centers on crafting a deep hierarchical structure adept at simulating human gaze patterns, utilizing the BING metric to pinpoint objects and their components within scenes at different scales. To emulate human visual dynamics, we introduce a Robust Deep Active Learning (RDAL) methodology, systematically creating gaze shift paths (GSPs) and capturing their profound representations. A key novelty of RDAL is its resilience to inaccuracies in labeling, employing a strategically designed sparse penalty framework that facilitates the exclusion of non-informative or irrelevant deep GSP attributes. Furthermore, we propose a manifold-regularized feature selector (MRFS) to isolate premium deep GSP features, concurrently developing a linear SVM for dancing scene recognition. Our method’s efficacy, validated through rigorous testing, not only showcased its enhanced performance across conventional scenic datasets but also highlighted the exceptional discriminating power of deep GSP features in a specialized dataset for recognizing different dancing actions. Finally, the dancing actions can be optimized using a probabilistic model.

Computed tomography (CT) imaging is widely applied to identify tumors and internal injuries in the body. CT image retargeting involves reducing the size of less important areas (like normal organs) either horizontally or vertically, while maintaining the size of key areas (such as affected organs) in the CT image. This retargeting process can significantly improve the way CT images are displayed, which can be advantageous for further educational tasks, e.g., optimally displaying CT images to medical students. This paper focuses on effectively merging multiple channels of perceptual visual features for retargeting CT images with intricate spatial layouts. The key objective is to develop an active learning model that identifies where human attention is directed in a scenery. To capture both semantically and visually important elements in each CT image, we use the BING objectness descriptor, which quickly and accurately localize objects or their parts across multiple scales. Herein, multi-channel low-level features describe each object-aware patch. In an effort to characterize how humans perceive important scenic elements, we propose a locality-preserved and interactive active learning (LIAL) approach. This method sequentially generates gaze shift paths (GSP) for each CT image. Due to the high dimensionality of such GSP feature, a distribution-preserved feature selection (DPFS) is designed to acquire a few highly discriminative GSP features. Last, these refined GSP features are combined by a Gaussian mixture model (GMM) to retarget CT images. Extensive empirical studies demonstrate the power of the designed method.

In the artificial intelligence (AI) community, accurately interpreting the semantics of complex sceneries is a critical component across various systems. This paper introduces an effective pipeline that intelligently merges multi-channel perceptual visual features to identify scenic images with intricate spatial layouts. Our focus is on developing a deep hierarchical model that proactively identifies where human gaze is directed in a scenery. Overall, our method includes three key modules. First, we employ the BING objectness descriptor for the swift and precise localization of objects or their elements across multiple scales within a scene. Meanwhile, an algorithm for the local-global fusion of features is formulated to represent each BING patch, integrating various low-level attributes from different channels. Second, to mimic the human process of identifying semantically or visually significant patches within a scenery, we employee an active learning algorithm to localize those scenic patches that are semantically or visually salient. They further constitute the so-called Gaze Shift Path (GSP). Finally, an aggregation-guided deep neural network is designed to calculate the deep GSP features, which are subsequently applied to a multi-label SVM to distinguish among various scenic categories. Empirical evaluations reveal that our method’s categorization accuracy outperforms existing models on six generic scenic datasets by $2\%\sim 4.5\%$ . Besides, we observe a higher stability of our method according to the repetitive experiments. Furthermore, our method demonstrates exceptional discriminative power on a specially compiled sports educational image collection, wherein the accuracy exceeds the second best performer by 8%. These results showed the huge potential to computationally discover human gaze behavior in different visual recognition tasks.

Effectively understanding the semantics of sophisticated sceneries is a key module in plenty of artificial intelligence (AI) systems. In this article, we optimally fuse multi-channel perceptual visual features for recognizing scenic pictures with complex spatial configurations, focusing on formulating a deep hierarchical model to actively discover human gaze allocation. In detail, to uncover semantically/visually important patches within each scenery, we utilize the BING objectness descriptor to rapidly and accurately localize multi-scale objects or their components. Subsequently, a local-global feature fusion scenario is proposed to dynamically combine the multiple low-level features from multiple scenic patches. To simulate how humans perceiving semantically/visually important scenic patches, we design a robust deep active learning (RDAL) paradigm that sequentially derives gaze shift path (GSP) and hierarchically learns deep GSP features in a unified architecture. Notably, the key advantage of RDAL is the high tolerance of label noise by adding an elaborately-designed sparse penalty. That is, the contaminated and redundant deep GSP features can be implicitly abandoned. Finally, the refined deep GSP features are integrated into a multi-label SVM for recognizing sceneries of different categories. Empirical comparisons showed that: 1) our method performs competitively on six generic scenery set (average accuracy 2%~4.3% higher than the second best performer), and 2) our deep GSP feature is particularly discriminative to our compiled sport educational image set (average accuracy 7.7% higher than the second best performer).

Objectives: Face masks have become important after the pandemic, but the change in voice when wearing a face mask is still unclear. The study investigated the effect of face masks on the acoustic and perceptual characteristics of voice quality associated with young and older Chinese adults., Methods: Voice samples of the sustained vowel /a/ and continuous speech produced by 44 older and 61 young adults with and without an ASTM level-3 surgical face mask were recorded and analyzed. Perceptual and acoustic parameters including mean fundamental frequency (F0) and intensity, perturbation measures (jitter and shimmer), harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPs), and long-term average spectrum (LTAS) measures were obtained and compared., Results: When comparing masked to unmasked voices, for both male and female speakers, F0 and intensity showed no significant changes, except for F0 of continuous speech, which increased significantly. Meanwhile, perturbation measures such as jitter and shimmer were reduced, while HNR and CPPs increased. In addition, LTAS measures included low-frequency mean spectral energy (MSE), high-frequency MSE, and spectral tilt (ST), which were different. For perceptual measures, the overall grade of dysphonia, and roughness were reduced, except for the breathiness among older male speakers, while the other vocal qualities were not changed. Between young and older speakers, significant differences in shimmer, CPPs, and perceived breathiness among male speakers, and low-frequency MSE among female speakers were found., Conclusion: Wearing a surgical mask appeared to change the perceived voice quality. This is supported by the change in perturbation and LTAS measures, and HNR and CPPs values. In addition, some differences between young and older adults were observed. Oral Communication effectiveness may be affected when wearing surgical masks due to changes in voice quality. Additionally, clinicians need to exercise hightened caution in evaluating the voice quality of clients when wearing face masks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Multimodal medical fusion images (MMFI) are formed by fusing medical images of two or more modalities with the aim of displaying as much valuable information as possible in a single image. However, due to the different strategies of various fusion algorithms, the quality of the generated fused images is uneven. Thus, an effective blind image quality assessment (BIQA) method is urgently required. The challenge of MMFI quality assessment is to enable the network to perceive the nuances between fused images of different qualities, and the key point for the success of BIQA is the availability of valid reference information. To this end, this work proposes a generative adversarial network (GAN) -guided nuance perceptual attention network (G2NPAN) to implement BIQA for MMFI. Specifically, we achieve the blind evaluation style via the design of a GAN and develop a Unique Feature Warehouse module to learn the effective features of fused images from the pixel level. The redesigned loss function guides the network to perceive the image quality. In the end, the class activation mapping supervised quality assessment network is employed to obtain the MMFI quality score. Extensive experiments and validation have been conducted in a database of medical fusion images, and the proposed method is superior to the state-of-the-art BIQA method., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tian and Zhang.)