Objective Evaluation Measures 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.

Clinical evaluation of post-surgical scar hyperaesthesia: a longitudinal observational pilot study.

The mechanisms underlying persistent scar pain are not fully elucidated and evidence for the clinical evaluation of scar pain is limited. This pilot observational study investigated participation data and sought to identify objective clinical scar evaluation measures for future trials. With ethical approval and consent, adults undergoing planned hand surgery were enrolled from one NHS hospital. At 1- and 4-months post-surgery scar thermal and mechanical pain thresholds were evaluated with quantitative sensory testing; peri-scar inflammation with infrared thermometry and pliability with durometry. Participation data were analysed with descriptive statistics; the association of clinical measures with patient reported scar pain was analysed. Twenty-one participants (22% eligible patients) enrolled before study closure due to the COVID-19 pandemic; 13 completed follow up. No adverse events or dropouts resulted from clinical scar evaluation. Seventy percent of participants reported undertaking topical, nonprescription scar treatment independently. Neuropathic Pain Symptom Inventory (NPSI) scores were dispersed across the score range, capturing variability in participant-reported scar symptoms. Scar morphology, pliability and inflammation were not associated with scar pain. Differences between scar and contralateral skin in thermal and mechanical pain sensitivity were identified. People with acute hand scars participate in clinical research and independently initiate scar treatment. Clinical testing of acute post-surgical hand scars is well tolerated. The NPSI demonstrates utility for exploring scar pain symptoms and may support the elucidation of mechanisms of persistent scar pain. Clinical tests of thermal and mechanical and sensitivity are promising candidate clinical measures of scar pain for future trials. Background: it is unknown why some scars remain painful long-term. We do not know if scar flexibility, inflammation or sensitivity to temperature or pressure relate to scar pain. We investigated if patients would enrol in scar research, if scar testing was tolerated and if clinical tests are useful for future scar studies. Study conduct: with ethical approval and consent, adult hand surgery patients were enrolled from one NHS hospital. Scar pain, inflammation and response to thermal, sharp and pressure tests were assessed at 1- and 4-months after surgery. Statistically, we analysed study participation, tolerance for clinical scar tests and if the scar tests related to scar pain. Findings: 21 participants (22% eligible patients) enrolled before study closure due to the COVID-19 pandemic; 13 completed follow up. No participants were injured due to scar testing. 70% of participants reported treating their scar independently. Neuropathic Pain Symptom Inventory (NPSI) allows participants to give a broad range of answers about their scar symptoms. Scores for clinical tests of scar flexibility and inflammation did not relate to participant-reported scar pain. Scars were more sensitive to tests of pin prick and cold than unaffected skin. What we learned: people with new hand scars participate in research and independently initiate scar treatment. Clinical testing of post-surgical hand scars is well tolerated. The NPSI is useful for exploring scar pain symptoms and may help us to learn about persistent scar pain. Pinprick and cold clinical tests may be useful objective pain tests for future scar research.

Performance: Differences in measuring performance

An important issue in human resources is the procedure of the employees’ performance evaluation. The appraisal is essential in the sense of employee appreciation and motivation. Most employers use a subjective performance evaluation of a single superior or a group of persons involved in the employee’s working processes. The subjective evaluation of a group or one person is often questioned about being appropriate. An often-named solution for a more objective criteria could be data driven performance measures. Professional sport provides a unique opportunity to compare objective and subjective performance evaluation measures. A data set of the German Bundesliga was used to test if the two different performance measure come to equal results. It is shown that differences in means exist but equivalence tests support the hypothesis that both measures could be treated as equal. In toto, it seems that in an environment where performance is relatively good to measure objective and subjective performance evaluations lead to equivalent results.

Multi-hop Information-based Graph Convolutional Network for Clustering

Clustering is an essential and demanding undertaking in data analysis. The combination of traditional neural networks and graph convolutional networks (GCNs) has been extensively discussed in clustering tasks, in which the deep clustering methods learn useful content information and the graph convolutional networks mine the structured neighboring information in the graph data. However, the existing works equally consider the importance of different features to clustering and only focus on the nearest neighboring information in the structured features, ignoring the features of the long distant neighbors in the multi-hop information, resulting in inferior performance. We propose a novel multi-hop information-based graph convolutional network (MIGCN) for clustering to overcome these disadvantages. Specifically, we fuse content features and structured features with adaptive weights, which are dynamically adjusted according to the training results. Meanwhile, we utilize a multi-hop information module to extract structured features. Moreover, we design a multi-supervision mechanism and guide the training and updating of the whole model. Our method achieves better results than previous deep clustering approaches because it takes into account the diverse features embedded in the neural network and a dynamic fusion strategy for clustering. Our model has undergone rigorous testing on widely-accepted standard datasets, employing both objective and subjective evaluation measures. The results consistently demonstrate that our model consistently outperforms state-of-the-art techniques, validating its superior performance.

A Novel Interval Iterative Multi-Thresholding Algorithm Based on Hybrid Spatial Filter and Region Growing for Medical Brain MR Images

Medical image segmentation is widely used in clinical medicine, and the accuracy of the segmentation algorithm will affect the diagnosis results and treatment plans. However, manual segmentation of medical images requires extensive experience and knowledge, and it is both time-consuming and labor-intensive. To overcome the problems above, we propose a novel interval iterative multi-thresholding segmentation algorithm based on hybrid spatial filter and region growing for medical brain MR images. First, a hybrid spatial filter is designed to perform on the original image, which can make full use of the spatial information while denoising. Second, the interval iterative Otsu method based on region growing is proposed to segment the original image and its filtering layer. The initial thresholds can be quickly obtained by region growing algorithm, which can reduce the time complexity. The interval iterative algorithm is used to optimize the thresholds. Finally, a weighted strategy is used to refine the segmentation results. The segmentation results of our proposed algorithm outperform other comparison algorithms in both subjective and objective evaluations. Subjectively, the obtained segmentation results have clear edges, complete and consistent regions. We use the uniformity measure (U) for objective evaluation, and the U value is significantly higher than other comparison algorithms. The proposed algorithm achieved an average U value of 0.9854 across all test images. The proposed algorithm can segment medical images well and expand the doctor’s ability to utilize medical images.

Registration of photoacoustic tomography vascular images: Comparison and analysis of automatic registration approaches

Image registration is crucial in the clinical application of photoacoustic tomography (PAT) for vascular growth monitoring. Aiming to find an optimized registration scheme for PAT vascular images acquired at different times and with varying imaging conditions, we compared and analyzed different commonly used intensity-based and feature-based automatic registration schemes. To further improve the registration performance, we proposed a new scheme that combines phase correlation with these commonly used intensity-based registration methods and compared their performances. The objective evaluation measures: peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), root mean square error (RMSE), and quantitative visual perception (jump percentage P), as well as subjective evaluation using mean opinion score (MOS), were combined to evaluate the registration performance. Results show that the feature-based approaches in this study were not suitable for PAT image registration. And by adding phase correlation as rough registration, the overall registration performance was improved significantly. Among these methods, the proposed scheme of phase correlation combined with mean square error (MSE) similarity measure and regular-step-gradient-descent optimizer provides the best visual effect, accuracy, and efficiency in PAT vascular image registration.

A Novel Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning.

Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals. Despite improving the speech quality, such approaches do not deliver required levels of speech intelligibility in everyday noisy environments. Intelligibility-oriented (I-O) loss functions have recently been developed to train DL approaches for robust speech enhancement. Here, we formulate, for the first time, a novel canonical correlation based I-O loss function to more effectively train DL algorithms. Specifically, we present a canonical-correlation based short-time objective intelligibility (CC-STOI) cost function to train a fully convolutional neural network (FCN) model. We carry out comparative simulation experiments to show that our CC-STOI based speech enhancement framework outperforms state-of-the-art DL models trained with conventional distance-based and STOI-based loss functions, using objective and subjective evaluation measures for case of both unseen speakers and noises. Ongoing future work is evaluating the proposed approach for design of robust hearing-assistive technology.

FlexLip: A Controllable Text-to-Lip System.

The task of converting text input into video content is becoming an important topic for synthetic media generation. Several methods have been proposed with some of them reaching close-to-natural performances in constrained tasks. In this paper, we tackle a subissue of the text-to-video generation problem, by converting the text into lip landmarks. However, we do this using a modular, controllable system architecture and evaluate each of its individual components. Our system, entitled FlexLip, is split into two separate modules: text-to-speech and speech-to-lip, both having underlying controllable deep neural network architectures. This modularity enables the easy replacement of each of its components, while also ensuring the fast adaptation to new speaker identities by disentangling or projecting the input features. We show that by using as little as 20 min of data for the audio generation component, and as little as 5 min for the speech-to-lip component, the objective measures of the generated lip landmarks are comparable with those obtained when using a larger set of training samples. We also introduce a series of objective evaluation measures over the complete flow of our system by taking into consideration several aspects of the data and system configuration. These aspects pertain to the quality and amount of training data, the use of pretrained models, and the data contained therein, as well as the identity of the target speaker; with regard to the latter, we show that we can perform zero-shot lip adaptation to an unseen identity by simply updating the shape of the lips in our model.

Pediatric Preventive Care in Middle-High Resource Countries-The Padova Chart for Health in Children.

ImportanceThe Padova Chart for Health in Children (PCHC) aims to gather the evidence of healthcare promotion and protection for chidren and adolescents (i.e., aged <18 y) into a single document in order to guide families, healthcare providers and social actors on healthy choices. No more than 2% of Europeans and North Americans aged <30 y have a healthy lifestyle. This, together with metabolic and brain plasticity during childhood, creates the ideal opportunity to implement preventive strategies. Guided interventions promoting healthy lifestyle in children and families therefore have a key role in abating the unprecedented pandemic of non-communicable diseases (NCDs) in adulthood.ObservationsThe PCHC is divided into four sections: nutrition, cardiovascular health, respiratory health, and mental and social health. Each section is structured in an ALICE approach (assessment, lobbying, intervention, call-for-action, evaluation): assessment of necessity, describing relevance to healthcare; lobbying to identify those who can effect the proposed interventions; interventions involving family, school and peers; a call-for-action to define priorities among the proposed interventions; and objective evaluation measures that can be applied on a population basis.Conclusions and RelevanceInterventions promoting health in childhood require joint action from multiple institutional, local and family representatives, with the shared goal of promoting health across the entire age group. These lifestyle interventions have the potential to change the lifetime risk trajectory for NCDs.

A perceptual measure for evaluating the resynthesis of automatic music transcriptions

This study focuses on the perception of music performances when contextual factors, such as room acoustics and instrument, change. We propose to distinguish the concept of “performance” from the one of “interpretation”, which expresses the “artistic intention”. Towards assessing this distinction, we carried out an experimental evaluation where 91 subjects were invited to listen to various audio recordings created by resynthesizing MIDI data obtained through Automatic Music Transcription (AMT) systems and a sensorized acoustic piano. During the resynthesis, we simulated different contexts and asked listeners to evaluate how much the interpretation changes when the context changes. Results show that: (1) MIDI format alone is not able to completely grasp the artistic intention of a music performance; (2) usual objective evaluation measures based on MIDI data present low correlations with the average subjective evaluation. To bridge this gap, we propose a novel measure which is meaningfully correlated with the outcome of the tests. In addition, we investigate multimodal machine learning by providing a new score-informed AMT method and propose an approximation algorithm for the p-dispersion problem.

Sustainable Assessment for Biomass Power Plant Location during the COVID-19 Pandemic

Renewable energy plays an important role in energy sustainability and environmental friendliness, for example in terms of biomass power, and is essential for generating electric power, which has a significant impact on sustainable energy for the future. The objective of this research, therefore, is to propose a sustainable method of assessment for biomass power plant site selection for electric power supply for field hospitals during the COVID-19 pandemic in Thailand. In this research, based upon overlay map layers as an empirical study, the pandemic, as well as environmental friendliness, are considered in Thailand. The Fuzzy Analytic Hierarchy Process (FAHP) has been applied in order to determine the weighting of the criteria. Furthermore, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) has also been applied in order to rank alternative locations. The contribution of this research lies in the development of an approach that is flexible in terms of sustainable assessment, in terms of both subjective and objective evaluation measures, in order to reduce bias in the decision-making process, as well as to include natural disaster assessment in providing guidelines for the location selection of a biomass power plant from the perspective of environment friendliness.

Reconstruction of scene using corneal reflection

Corneal reflection extracted from an eye image identifies the relationship between the subject of the image and the scene in front of the subject. The reconstructed scene from corneal reflection provides detailed information about the environment opposite to the subject. It also provides scrutiny about any critical scenario, a subject is encountered with. This research area has significant applications in computer vision, human-computer interaction, psychology, and image forgery detection. Digital image processing and computer vision techniques have been used to reconstruct the scene from cornea image. The proposed model involved the following steps, i.e., identification of the corneal area in an eye, unnecessary reflection removal from cornea surface, developing eye geometric model to correct the spherical effect of the eye, and implementation of super-resolution (SR) algorithm to reconstruct the lost visual information present in the environment. The proposed study is able to reconstruct the SR scene image from cornea image. The effectiveness of the study is evaluated by using subjective as well as objective evaluation measures. Some useful insights related to cornea reflection construction have been described to make this study more effective.

Objective evaluation of the ``scratch collapse test'' for the diagnosis of carpal tunnel syndrome

PurposeEvaluate the Scratch Collapse Test in an objective way, by replacing the subjective evaluation made by the physician with an objective evaluation measure, made with a digital dynamometer. MethodsObservational study carried out, in 90 patients divided into three groups of 30 patients, taking into account the electromyographic study of the median nerve in the carpal tunnel (no alteration, moderate, severe).The external rotation of the shoulder was measured in four different situations (no scratch, scratch over the carpal tunnel, scratch in the dorsum of the wrist and scratch in the shoulder). ResultsThere were no statistical differences in the result of the strength in any of the four different situations in patients without carpal tunnel of with moderate carpal tunnel syndrome. However, there were statistical differences between the basal measurement (without scratching) and the measurement after tunnel scratching in patients with severe carpal tunnel syndrome. But this statistical difference was only 0.08 kg in the average measure, and this difference is clinically undetectable and far for producing a real collapse of the external rotation of the shoulder. ConclusionThe Scratch Collapse Test is not a valid diagnostic exam for carpal tunnel syndrome if the strength is measured in an objective manner.

An integrated FMEA and MCDA based risk management approach to support life extension of subsea facilities in high-pressure–high-temperature (HPHT) conditions

The majority of facilities installed in offshore oil and gas fields during the 1980s and 1990s were designed to operate in ‘normal’ conditions. However, during the operational life of the fields, some new high pressure/high temperature (HPHT) wells may be discovered and tied back to older facilities. Operating these facilities beyond their design parameters in harsh environments may lead to catastrophic failures, resulting in significant economic losses and environmental problems. Managing the risks associated with failure of ageing subsea facilities in HPHT environments is considered as a very complex and critical task. To overcome such challenge, there is a need for development of decision-making methods that are capable of estimating precisely the risks associated with HPHT conditions as well as prioritising the risk mitigation and remediation strategies. This paper aims to propose an integrated risk management framework – based on Failure Mode and Effects Analysis (FMEA) approach and a hybrid Multi-Criteria Decision Analysis (MCDA) model – for evaluating the risks and prioritising mitigation strategies over the extended lifetime of subsea facilities in HPHT environments. For the purpose of illustrating the model, a case study of subsea manifold and flowlines is provided and the results are evaluated and discussed. Our findings indicate that the proposed approach offers significant improvement to the classical risk management processes applied to subsea oil and gas facilities as it can assist asset managers, risk analyst, regulators and policy makers with a decision model which considers both subjective (qualitative) judgements and objective (quantitative) evaluation measures.

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.

Health Related Quality of Life (HRQoL) in Hypertensive Patients

Hypertension also called as increased blood pressure (BP) is a chronic disorder in which BP is persistently elevated in the main systemic arteries. The main risk factor for cardiovascular disorder is hypertension. It is common in almost all countries. It makes a silent and gradual progress. Quality of life is a wide term which mainly affects the physical health, psychological condition, level of independence, social relationships and personal beliefs of a person. The HRQoL in people with hypertension can be mostly affected by the disease condition itself or due to the adverse effects of the treatment. Determining the quality of life of patients with hypertension has become an important issue. The HRQoL of hypertensive patients can be improved if the treatment and lifestyle modifications are done correctly. Counseling the patients with hypertension regarding risk factors, lifestyle modifications and medication adherence can also improve the HRQoL. Several questionnaires are available for assessing the HRQoL of people with different disorders. This questionnaire or instrument can be categorized into two types: the questionnaires for a specific disease and the general questionnaire. The SF instrument was introduced in the United States of America by the RANDS Corporation’s Health Insurance researches. It has now been translated to more than 120 world languages. Short form instrument is divided into different types which includes SF-36, SF-12, SF-20 and many more. EuroQol created EQ5D as a standardized method for assessing health status to provide a clear, objective health measure for medical and economic evaluation. . The EQ5D instrument comprises of three versions: EQ-5D-5L, EQ-5D-3L and EQ-5D-Y. WHOQoL- BREF, is a generic questionnaire administered on its own which contain 26 items. The MINICHAL was developed or introduced in 2001 in Spain which contains 17 questions. The word MINICHAL was obtained from “Mini Cuestionario de Calidad de Vida en Hypertensión Arterial.

Binaural Interaction in Tinnitus Patients

The auditory brainstem response (ABR) is a commonly used objective clinical measure for hearing evaluation. It can be also used to draw conclusions about the functioning of distinct stages of the auditory pathway including the binaural processing stages using the binaural interaction component (BIC) of the ABR. Objective: To study binaural processing in normal hearing subjects complaining of tinnitus. Methods: Sixty cases with bilateral normal peripheral hearing were included in this work, divided into 2 groups, i.e., group 1 (comprised of 30 healthy subjects representing the control group) and group 2 (comprised of 30 subjects with tinnitus representing the study group). All of the subjects were submitted to a basic audiological evaluation (including pure tone audiometry, speech audiometry, and immittancemetry) and ABR audiometry recorded in monaural and then binaural conditions. Results: In monaural recording, the tinnitus group showed significantly delayed latencies of waves I, III, and V in addition to significantly reduced wave I and III amplitudes when compared with the controls. Similar significant findings were found when binaural ABR responses were compared between both groups. Comparing BIC between both groups showed significant earlier BIC for latencies of waves I and V in the control group, while the BIC for amplitudes showed similar results in both groups. Conclusions: These finding suggest the presence of binaural processing deficits in tinnitus patients at different levels along the ascending auditory pathway.

Improved phase aware speech enhancement using bio-inspired and ANN techniques

The phase modification of noisy speech signal plays a crucial role in speech enhancement (SE). In the recent past, many speech denoising algorithms have been proposed using the modification of phase information which depends on the scaling factor computed from the noise level. The performance measures of SE is significantly affected by this scaling factor and noise level estimation. However, in these algorithms, the parameters are not optimally tuned for the different noise conditions and also in some cases, the background noise is presumed to be stationary. Further, no earlier attempt has been made to obtain adaptive models which can establish the relationship between noise levels and scaling factor. Being motivated by these observations an attempt has been made in this paper to develop a neural network based model which is capable of properly estimating this scaling factor from the noise level. In the current work, a popular and efficient bio-inspired technique known as firefly algorithm is employed to determine the best possible scaling factor for each noise level. In addition, a relationship is established between noise level and scaling factor using trigonometric functional expansion based artificial neural network. An effective nonstationary noise estimation strategy is also incorporated in the proposed algorithm. Simulation-based experiments are performed to evaluate the effectiveness of the proposed SE algorithm and compared with other six standard SE algorithms using standard database. The analysis of the simulation results demonstrates that the proposed method outperforms the others in terms of both subjective and objective evaluation measures.

Insight on the utilization of the noise estimation techniques in phase aware modified multiband spectral subtraction

ABSTRACTThe standard technique of all Spectral Subtraction algorithms that are used for Speech Enhancement (SE) is to find out the clean speech by subtracting the noise estimate from the noisy speech spectrum. So the essential criterion is the accuracy of noise estimation (NE) and it affects the potency of the SE. The traditional NE algorithms use voice activity detection (VAD) for noise estimation, which is troublesome at the cases of low SNR condition and when the noise characteristics are not stationary. The effect of noise is also different in different frequency bands of the noisy speech spectrum. In this paper, several NE techniques are implemented along with a phase aware and frequency dependent modified multi-band spectral subtraction. To give additional intelligibility to SE, the phase of the enhanced signal is modified using the phase spectrum compensation function. Finally, the mentioned methods are simulated and compared using several objective evaluation measures with the help of standard speech database.

An optimal solution based on image fusion for blood vessel diameter measurements in cerebral angiographies

The main goal of this study is an accurate extraction of the brain vascular structure to improve detection and to enhance the blood vessels in the human brain. Data provided by Canny and Frangi edge detection algorithms are evaluated and then these disparate data are combined with a fusion algorithm for comparison purposes. This investigation is performed in an edge detection framework. The performance of the proposed methods is evaluated by conducting measurements on the diameter of the brain blood vessels in cerebral angiography images. The average diameters of the blood vessels provided by Frangi, Canny and the fusion algorithms are measured. To highlight the improvements brought by data fusion, an objective evaluation measure based on edge structural similarity and error rate have been made. Also, the superiority of the fusion algorithm in terms of image quality is proved.