Search Results (17)

Smart cities provide an ideal framework for the integration of advanced healthcare applications, such as early Alzheimer’s Disease (AD) detection that is essential to facilitate timely interventions and slow its progression. In this context, speech analysis, combined with Artificial Intelligence (AI) techniques, has emerged as a promising approach for the automatic detection of AD, as vocal biomarkers can provide valuable indicators of cognitive decline. The proposed approach focuses on two key goals: minimizing computational overhead while maintaining high accuracy, and improving model interpretability for clinical usability. To enhance efficiency, the framework incorporates a data quality method that removes unreliable speech segments based on duration thresholds and applies Shapley Additive Explanations (SHAP) to select the most influential acoustic features. SHAP is also used to improve interpretability by providing global and local explanations of model decisions. The final model, that is based on Extreme Gradient Boosting (XGBoost), achieves an F1-Score of 0.7692 on the ADReSS dataset, showing good performance and a satisfactory level of clinical utility. This work highlights the potential of explainable AI to bridge machine learning techniques with clinically meaningful insights in the domain of AD detection from speech. Full article

Crowdfunding videos have become a crucial tool for entrepreneurs seeking financial support, with audio design playing a critical role in attracting potential investors. However, research on how audio features influence crowdfunding success remains limited. This study uses audio analysis techniques to examine data from 4500 crowdfunding campaigns on the Kickstarter platform between 2013 and 2016, investigating the impact of audio features on crowdfunding success rates. Grounded in the signaling theory, we posited four hypotheses suggesting that speech rate, loudness, pitch, and emotional arousal would each exhibit an inverted U-shaped relationship with crowdfunding success rates. Through data analysis, we found that moderate levels of speech rate, loudness, pitch, and emotional arousal significantly enhanced crowdfunding success, whereas extremes in these vocal characteristics had a detrimental effect. Our findings not only extend the application of audio analysis in the crowdfunding domain, but also provide empirical evidence for the influence of audio features on crowdfunding success. This research offers practical guidance for project initiators in developing promotional strategies and for platforms in optimizing user experience. Full article

Non-violent (vocal) extremists are at the centre of inflamed debates in the UK as they use their freedom of speech to legally oppose key democratic values and national authorities while targeting specific groups in the society as alleged enemies. This paper aims to explore the radicalisation pathways of individuals in the UK who hold radical and extreme ideas but who have not (yet) committed any offence. More precisely, this paper aims to uncover the role (if any) played by non-violent extremist groups in pathways to radicalisation. To do so, this paper focuses on people supported by the Channel programme, the main early de-radicalisation programme within the Prevent Scheme. This paper innovatively uses first-hand data reflecting the views of Channel practitioners on radicalisation pathways. This study concludes that non-violent extremist groups (as structures) still play a role in familiarising/acculturating individuals with specific ideological concepts. However, the processes of identity building and resource mobilisation seem to be strongly agent-led when observing Channel population in the UK. Full article

Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith–Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal–Wallis test and post hoc analysis with Dunn–Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes. Full article

Communicating under extreme noise conditions remains challenging in spite of higher-order noise-canceling microphones, throat microphones, and signal processing. Both natural and human-made background ambient noise can disturb the conveyance of information because of high noise levels. Noise cancellation, which is used frequently in audio technology, has limits in noise reduction and does not guarantee clear vocal pickup in these severe situations. A contact microphone that is attached directly to the medium of interest has the potential to pick up vocal signals with reduced noise. In this study, an electrostatic transducer with an elastomer layer that is impedance-matched to the human body is used to pick up speech sounds through constant contact on the chin and cheek. By attaching the wearable device directly to the skin, the medium of air is bypassed, and airborne noise is passively canceled. Because of the acoustic impedance-matched layer, the sensor is more sensitive to low frequencies under 500 Hz, so frequency equalization was implemented to flatten the frequency response throughout the vocal range. The perceptual evaluation of speech quality (PESQ) scores of the wearable device with equalization averaged around 2.6 on a scale from –0.5 to 4.5. Speech recordings were also collected in a noise field of 85 dB, and the performance was compared to a cardioid lapel mic, a cardioid dynamic mic, and an omnidirectional condenser mic. The recordings revealed a significantly reduced presence of white noise in the contact sensor. This study provides preliminary results that show potential vocal applications for a wearable impedance-matched sensor. Full article

In social interactions, people who are perceived as competent win more chances, tend to have more opportunities, and perform better in both personal and professional aspects of their lives. However, the process of evaluating competence is still poorly understood. To fill this gap, we developed a two-step empirical study to propose a competence evaluation framework and a predictor of individual competence based on multimodal data using machine learning and computer vision methods. In study 1, from a knowledge-driven perspective, we first proposed a competence evaluation framework composed of 4 inner traits (skill, expression efficiency, intelligence, and capability) and 6 outer traits (age, eye gaze variation, glasses, length-to-width ratio, vocal energy, and vocal variation). Then, eXtreme Gradient Boosting (XGBoost) and Shapley Additive exPlanations (SHAP) were utilized to predict and interpret individual competence, respectively. The results indicate that 8 (4 inner and 4 outer) traits (in descending order: vocal energy, age, length-to-width ratio, glasses, expression efficiency, capability, intelligence, and skill) contribute positively to competence evaluation, while 2 outer traits (vocal variation and eye gaze variation) contribute negatively. In study 2, from a data-driven perspective, we accurately predicted competence with a cutting-edge multimodal machine learning algorithm, low-rank multimodal fusion (LMF), which exploits the intra- and intermodal interactions among all the visual, vocal, and textual features of an individual’s competence behavior. The results indicate that vocal and visual features contribute most to competence evaluation. In addition, we provided a Chinese Competence Evaluation Multimodal Dataset (CH-CMD) for individual competence analysis. This paper provides a systemic competence framework with empirical consolidation and an effective multimodal machine learning method for competence evaluation, offering novel insights into the study of individual affective traits, quality, personality, etc. Full article

Passive acoustics are widely used to monitor the presence of dolphins in the marine environment. This study aims to introduce a low-cost and homemade approach for assembling a complete underwater microphone (i.e., the hydrophone), employing cheap and easy to obtain components. The hydrophone was assembled with two piezo disks connected in a balanced configuration and encased in a plastic container filled with plastic foam. The hydrophone’s performance was validated by direct comparison with the commercially available AS-1 hydrophone (Aquarian Hydrophones, Anacortes, U.S.) on different underwater acoustic signals: artificial acoustic signals (ramp and multitone signals) and various dolphin vocalizations (whistle, echolocation clicks, and burst pulse signals). The sensitivity of the device’s performance to changes in the emission source position was also tested. The results of the validation procedure on both artificial signals and real dolphin vocalizations showed that the significant cost savings associated with cheap technology had a minimal effect on the recording device’s performance within the frequency range of 0–35 kHz. At this stage of experimentation, the global cost of the hydrophone could be estimated at a few euros, making it extremely price competitive when compared to more expensive commercially available models. In the future, this effective and low-cost technology would allow for continuous monitoring of the presence of free-ranging dolphins, significantly lowering the total cost of autonomous monitoring systems. This would permit broadening the monitored areas and creating a network of recorders, thus improving the acquisition of data. Full article

In one study on vocal emotion recognition using noise-vocoded speech (NVS), the high similarities between modulation spectral features (MSFs) and the results of vocal-emotion-recognition experiments indicated that MSFs contribute to vocal emotion recognition in a clean environment (with no noise and no reverberation). Other studies also clarified that vocal emotion recognition using NVS is not affected by noisy reverberant environments (signal-to-noise ratio is greater than 10 dB and reverberation time is less than 1.0 s). However, the contribution of MSFs to vocal emotion recognition in noisy reverberant environments is still unclear. We aimed to clarify whether MSFs can be used to explain the vocal-emotion-recognition results in noisy reverberant environments. We analyzed the results of vocal-emotion-recognition experiments and used an auditory-based modulation filterbank to calculate the modulation spectrograms of NVS. We then extracted ten MSFs as higher-order statistics of modulation spectrograms. As shown from the relationship between MSFs and vocal-emotion-recognition results, except for extremely high noisy reverberant environments, there were high similarities between MSFs and the vocal emotion recognition results in noisy reverberant environments, which indicates that MSFs can be used to explain such results in noisy reverberant environments. We also found that there are two common MSFs (MSKT${}_k$ (modulation spectral kurtosis) and MSTL${}_k$ (modulation spectral tilt)) that contribute to vocal emotion recognition in all daily environments. Full article

Parkinson’s Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient’s daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson’s Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting. Full article

Extremism in both its vocal and violent forms is a core topic of research, as well as a priority issue standing at the top of national and international security agendas. While most of the literature is still focused on violent forms of extremism, an emerging research trend is looking at vocal extremism and radicalisation as crucial steps to understand, as Neumann said “what happens before the bomb goes off”. Within this new trend, scholars are interested in exploring the ideology of extremist groups (rather than just their methods) and the frames (schemata of interpretation) they disseminate in order to win more followers and fidelise their members. Based on the author’s previous research, as well as on relevant data extracted from the manifestos and relevant publications of emblematic groups of both ideologies, this paper compares Islamists and Eco-radicals as two forms of extremism fighting the Western-sponsored modernisation process. By exploring the meaning of “modernity”, as well as the role played by frames, this research sheds light on three common frames present in both ideologies, i.e., the enemy to fight, the victims to protect, and the change to achieve. These three frames are the linchpin of the discourses of both forms of extremism. By innovatively unpacking these frames from a comparative perspective, this research offers new insights into the impact of modernity on the development of alternative and extremist ideologies. Full article

Background: The survival of extremely low birth weight infants (ELBW) has increased worldwide. Even in the absence of major disabilities, ELBW infants show difficulty in simple language functions. It is relevant to assess early abilities, which are the base of early linguistic skills, in order to implement customized intervention programs in ELBW infants. Aims: To evaluate communication precursors of language development in ELBW infants at 12 and 24 months of correct age (C.A). To investigate the correlation of linguistic and communicative prerequisites with mental development outcome at 24 months CA. Method: 52 ELBW neonates (mean gestational age 26.6 weeks, mean birth weight was 775 g) who were admitted to the neonatal intensive care unit of the University Hospital of Modena, were enrolled. Data were collected from archived audio-video recordings of neurodevelopmental follow-up visits. Video analysis of communicative and linguistic developmental was performed at 12 and 24 months CA. Neurodevelopmental outcome was evaluated with Mental Developmental Scales (GMDS-R). Results: The video-analysis showed that infants at 12 months CA used predominantly eye contacts and gestural turns, while vocal turns were scant. At 24 months CA, a significant change in eye contacts, vocal turns, gestural turns, and utterances (p < 0.001) occurred. The total number of utterances (p = 0.036) and eye contacts (p = 0.045) were significantly correlated to the Development Quotient (DQ) of Hearing and Language scale. Moreover, a significant correlation was found with the Personal-Social scale vocal turns (p = 0.009) and the total number of utterances (p = 0,02). Finally, the Global Quotient of the GMDS-R was related to the Vocal Turns (p = 0.034) and the total number of Utterances (p = 0.013). Conclusions: ELBW infants at 12 months CA use predominantly eye contacts and gestural turns to communicate with adults. At 24 months CA, the child’s communicative intention evolves from gestural to verbal communication. The latter is characterized by an increase in both vocal turns and the number of utterances produced during interaction. The video analysis we implement appears to be a sensitive tool for early assessment of communication and language development and to refine early intervention Full article

During the ovarian cycle in domestic dogs, females do not accept males during the first days of estrus but become attractive to males from the beginning of proestrus, with this attractiveness persisting until the end of the estrus phase. It is believed that increased estradiol is responsible for the female attractiveness to the males. In this paper we describe the case of strong, but atypical attractiveness of a castrated male to various, adult, intact males, influenced by the emitted semiochemical signals. Any significant changes in the level of hormones typically involved in the process connected with estrus and responsible for sexual arousal in the males were assessed. The case animal was a 4 year old castrated male Border Collie that was extremely attractive to various males, which presented high levels of sexual arousal, with intensive sniffing and licking of the preputial area, specific vocalization, increased salivation and, finally, mating attempts. Clinical examination of the castrated male revealed a lack of testes in the scrotum and abdominal cavity confirmed by USG. Laboratory tests indicated basal levels of estradiol, testosterone, and progesterone (15.23 pg/mL, <0.05 ng/mL, 0.25 ng/mL), and sex was confirmed via cytogenetic and molecular analysis. Chemical analysis (HS-SPME) of the urine indicated a huge similarity to the profile obtained from a bitch in estrus, with an elevated level of acetophenone, which has been previously postulated in the literature as being a characteristic of the estrus phase in female domestic dogs. This case presented very atypical sexual attractiveness, particularly when taking into account the basal levels of hormones which, according to current knowledge, are responsible for the creation of attractiveness. As a hypothesis requiring verification, we propose the idea of involvement of other hormones in the creation of incidental attractiveness or increased production of compounds responsible for attractiveness (sex pheromones) resulting from metabolic events unrelated to reproductive processes. To our knowledge it is the first described case presenting this phenomenon, which, with more detailed study, could shed new light on the process of creation of sexual attraction in the domestic dog. Full article

As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal changes and impairments at the early stages of PD. In this study, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect PD patients based on their vocal features. The first model extracted and reduced the principal components of vocal features based on the explained variance of each feature using PCA. For the first time, the second model used a novel Deep Neural Network (DNN) of an SAE, consisting of multiple hidden layers with L1 regularization to compress the vocal features into lower-dimensional latent space. In both models, reduced features were fed into the SVM as inputs, which performed classification by learning hyperplanes, along with projecting the data into a higher dimension. An F1-score, a Mathews Correlation Coefficient (MCC), and a Precision-Recall curve were used, along with accuracy to evaluate the proposed models due to highly imbalanced data. With its highest accuracy of 0.935, F1-score of 0.951, and MCC value of 0.788, the probing results show that the proposed model of the SAE-SVM surpassed not only the former model of the PCA-SVM and other standard models including Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Random Forest (RF), but also surpassed two recent studies using the same dataset. Oversampling and balancing the dataset with SMOTE boosted the performance of the models. Full article

This review attempted to collate and synthesize information on goat welfare and production constraints during heat stress exposure. Among the farm animals, goats arguably are considered the best-suited animals to survive in tropical climates. Heat stress was found to negatively influence growth, milk and meat production and compromised the immune response, thereby significantly reducing goats’ welfare under extensive conditions and transportation. Although considered extremely adapted to tropical climates, their production can be compromised to cope with heat stress. Therefore, information on goat adaptation and production performance during heat exposure could help assess their welfare. Such information would be valuable as the farming communities are often struggling in their efforts to assess animal welfare, especially in tropical regions. Broadly three aspects must be considered to ensure appropriate welfare in goats, and these include (i) housing and environment; (ii) breeding and genetics and (iii) handling and transport. Apart from these, there are a few other negative welfare factors in goat rearing, which differ across the production system being followed. Such negative practices are predominant in extensive systems and include nutritional stress, limited supply of good quality water, climatic extremes, parasitic infestation and lameness, culminating in low production, reproduction and high mortality rates. Broadly two types of methodologies are available to assess welfare in goats in these systems: (i) animal-based measures include behavioral measurements, health and production records and disease symptoms; (ii) resources based and management-based measures include stocking density, manpower, housing conditions and health plans. Goat welfare could be assessed based on several indicators covering behavioral, physical, physiological and productive responses. The important indicators of goat welfare include agonistic behavior, vocalization, skin temperature, body condition score (BCS), hair coat conditions, rectal temperature, respiration rate, heart rate, sweating, reduced growth, reduced milk production and reduced reproductive efficiency. There are also different approaches available by which the welfare of goats could be assessed, such as naturalistic, functional and subjective approaches. Thus, assessing welfare in goats at every production stage is a prerequisite for ensuring appropriate production in this all-important species to guarantee optimum returns to the marginal and subsistence farmers. Full article

The scientific study of teamwork in the context of long-term spaceflight has uncovered a considerable amount of knowledge over the past 20 years. Although much is known about the underlying factors and processes of teamwork, much is left to be discovered for teams who operate in extreme isolation conditions during spaceflights. Thus, special considerations must be made to enhance teamwork and team well-being for long-term missions during which the team will live and work together. Being affected by both mental and physical stress during interactional context conversations might have a direct or indirect impact on team members’ speech acoustics, facial expressions, lexical choices and their physiological responses. The purpose of this article is (a) to illustrate the relationship between the modalities of vocal-acoustic, language and physiological cues during stressful teammate conversations, (b) to delineate promising research paths to help further our insights into understanding the underlying mechanisms of high team cohesion during spaceflights, (c) to build upon our preliminary experimental results that were recently published, using a dyadic team corpus during the demanding operational task of “diffusing a bomb” and (d) to outline a list of parameters that should be considered and examined that would be useful in spaceflights for team-effectiveness research in similarly stressful conditions. Under this view, it is expected to take us one step towards building an extremely non-intrusive and relatively inexpensive set of measures deployed in ground analogs to assess complex and dynamic behavior of individuals. Full article