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We investigated the impact of hearing loss on the neural processing of speech. Using a forward modelling approach, we compared the neural responses to continuous speech of 14 adults with sensorineural hearing loss with those of age-matched normal-hearing peers. Compared with their normal-hearing peers, hearing-impaired listeners had increased neural tracking and delayed neural responses to continuous speech in quiet. The latency also increased with the degree of hearing loss. As speech understanding decreased, neural tracking decreased in both populations; however, a significantly different trend was observed for the latency of the neural responses. For normal-hearing listeners, the latency increased with increasing background noise level. However, for hearing-impaired listeners, this increase was not observed. Our results support the idea that the neural response latency indicates the efficiency of neural speech processing: More or different brain regions are involved in processing speech, which causes longer communication pathways in the brain. These longer communication pathways hamper the information integration among these brain regions, reflected in longer processing times. Altogether, this suggests decreased neural speech processing efficiency in HI listeners as more time and more or different brain regions are required to process speech. Our results suggest that this reduction in neural speech processing efficiency occurs gradually as hearing deteriorates. From our results, it is apparent that sound amplification does not solve hearing loss. Even when listening to speech in silence at a comfortable loudness, hearing-impaired listeners process speech less efficiently. ispartof: EUROPEAN JOURNAL OF NEUROSCIENCE vol:55 issue:6 pages:1671-1690 ispartof: location:France status: published

With selective auditory attention (SAA), the human brain can cope with dynamic natural environments efficiently by prioritizing relevant auditory information while filtering out the rest. Auditory saliency describes how bottom-up auditory attention is attracted by local acoustic properties and plays an important role in SAA. In existing neuroimaging studies, the neural processing of auditory saliency has usually been explored via strictly controlled laboratory paradigms with abstract stimuli, overlooking the intrinsic functional interactions among spatially distributed brain networks underlying auditory saliency. In addition, it is unclear how auditory saliency related neural responses vary in auditory information in different semantic categories. In this study, we explored functional brain networks underlying auditory saliency via a multivariate brain decoding approach, in which naturalistic auditory excerpts in three semantic categories (pop music, classic music and speech) were used as stimuli in functional magnetic resonance imaging (fMRI). Our experimental results demonstrate that: 1) Dynamic auditory saliency can be decoded with relatively high accuracy from fMRI brain activities; 2) The neural processing of auditory saliency in different types of auditory information on the one hand shares some common brain regions (e.g., the primary auditory cortex), on the other hand recruits distinct brain regions/networks, e.g., the sensorimotor network and working memory network for classic music and the auditory-language network for speech. Our study provides complementary evidence to the neural processing of auditory saliency under naturalistic experience.

Recommender systems, which aim to provide personalized suggestions for users, have proven to be an effective approach to cope with the information overload problem existing in many online applications and services. In this paper, we target two specific sequential recommendation tasks, next music recommendation and next new music recommendation, to predict the next (new) music piece that users would like based on their historical listening records. In current music recommender systems, various kinds of auxiliary/side information, e.g., item contents and users' contexts, have been taken into account to facilitate user/item preference modeling and have yielded comparable performance improvement. Despite the gained benefits, it is still a challenging and important problem to fully exploit sequential music listening records due to the complexity and diversity of interactions and temporal contexts among users and music, as well as the dynamics of users' preferences. To this end, this paper proposes a novel Attentive Temporal Point Process (ATPP) approach for sequential music recommendation, which is mainly composed of a temporal point process model and an attention mechanism. Our ATPP can effectively capture the long- and short-term preferences from the sequential behaviors of users for sequential music recommendation. Specifically, ATPP is able to discover the complex sequential patterns from the interaction between users and music with the temporal point process, as well as model the dynamic impact of historical music listening records on next (new) music pieces adaptively with an attention mechanism. Comprehensive experiments on four real-world music datasets demonstrate that the proposed approach ATPP outperforms state-of-the-art baselines in both next and next new music recommendation tasks.

Alternative text is critical in communicating graphics to people who are blind or have low vision. Especially for graphics that contain rich information, such as visualizations, poorly written or an absence of alternative texts can worsen the information access inequality for people with visual impairments. In this work, we consolidate existing guidelines and survey current practices to inspect to what extent current practices and recommendations are aligned. Then, to gain more insight into what people want in visualization alternative texts, we interviewed 22 people with visual impairments regarding their experience with visualizations and their information needs in alternative texts. The study findings suggest that participants actively try to construct an image of visualizations in their head while listening to alternative texts and wish to carry out visualization tasks (e.g., retrieve specific values) as sighted viewers would. The study also provides ample support for the need to reference the underlying data instead of visual elements to reduce users' cognitive burden. Informed by the study, we provide a set of recommendations to compose an informative alternative text.

This study focuses on identifying effective features for controlling speech to increase speech intelligibility under adverse conditions. Previous approaches either cancel noise throughout speech presentation or preprocess speech by controlling its intensity and/or spectra. Among them, a method based on modulation transfer function theory, inverting the environmental effects to anticipate attenuation of speech modulation spectrum, shows excellent potential due to its systematic and explicit derivation of intelligibility enhancement against environmental smears. However, strictly following the inverse modulation transfer function is dangerous and inefficient as important speech features can be damaged, and it costs lots of energy to boost all smeared regions. This study takes a different approach: analyzing the relations of smeared modulation spectra by the environments for intelligibility to extract effective modifying features. First, we conduct listening tests for intelligibility in noise with different types of enhanced speech. Next, we extract acoustic and modulation frequency components in the smeared modulation spectra by noise showing high correlation with intelligibility scores. Finally, we examine the intelligibility benefits of modifying these components by performing listening tests. The results show that these components effectively increase intelligibility by at most 18%, which demonstrates that our concept is valid.

Mathematical calculation usually requires sustained attention to manipulate numbers in the mind, while listening to light music has a relaxing effect on the brain. The differences in the corresponding brain functional network topologies underlying these behaviors remain rarely known. Here, we systematically examined the brain dynamics of four behaviors (resting with eyes closed and eyes open, tasks of music listening and mental calculation) using 64-channel electroencephalogram (EEG) recordings and graph theory analysis. We developed static and dynamic minimum spanning tree (MST) analysis method and demonstrated that the brain network topology under mental calculation is a more line-like structure with less tree hierarchy and leaf fraction; however, the hub regions, which are mainly located in the frontal, temporal and parietal regions, grow more stable over time. In contrast, music-listening drives the brain to exhibit a highly rich network of star structure, and the hub regions are mainly located in the posterior regions. We then adopted the dynamic dissimilarity of different MSTs over time based on the graph Laplacian and revealed low dissimilarity during mental calculation. These results suggest that the human brain functional connectivity of individuals has unique dynamic diversity and flexibility under various behaviors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-021-00027-w.

OBJECTIVES The objective of this study was to evaluate the influence of music versus speech on a listener's signal-to-noise ratio (SNR) preferences when listening in a noise background. SNR preferences were quantified using acceptable noise level (ANL) and preferred SNR metrics. The measurement paradigm for ANL allows the listener to adjust the level of background noise while listening to the target at their most comfortable loudness level. A higher ANL indicates less tolerance for noise and a lower ANL indicates high tolerance for noise. The preferred SNR is simply the SNR the listener prefers when attending to a target in a fixed-amount (level) of background noise. In contrast to the ANL, the listener does not have control over the noise. Rather, they are only able to manipulate the target level. The first aim of the study was to determine if listeners' tolerances for noise, quantified using the ANL, when listening to music is different from that when listening to speech. The second aim of the study was to determine if listeners' tolerances for noise, quantified using their preferred SNR, when listening to music is different from that when listening to speech. The third aim of the study was to quantify the relationship between ANL and preferred SNR. DESIGN Ninety-nine normal-hearing, native-English speakers participated in this study. The ANL and preferred SNR were measured for speech and music targets. Music targets included two variations (with lyrics and without lyrics) of the song "Rocky Top." Measurements were made in the sound field at 0° azimuth, 1.5 m from a loud speaker. For both ANL and preferred SNR, targets were presented in 12-talker babble noise. The level of the noise was adjusted by the listener during ANL measurement but was fixed in level during the preferred SNR measurement (75 dB A). Repeated-measures analysis of variance was performed to identify any significant effect of target on the ANL and preferred SNR. Correlation analysis was performed to evaluate the relationship between ANL and preferred SNR. RESULTS Findings demonstrate a significant effect of target on ANL and preferred SNR. ANLs were highest for speech (mean = 7.2 dB), followed by music with lyrics (6.1 dB), and music without lyrics (2.5 dB). Preferred SNRs were highest for music with lyrics (mean = 2.3 dB), followed by speech (1.2 dB), and music without lyrics (-0.1 dB). A listener's ANL for a given target was strongly correlated with their ANL for a different target (the same was true for preferred SNR); however, ANL for a given target was not a statistically significant predictor of preferred SNR for the same target. CONCLUSIONS When listening in a background of noise, the listener's tolerance for noise depends on the target to which they are attending, whether music or speech. This dependence is especially evident for ANL measures, and less so for preferred SNR measures. Despite differences in ANL and preferred SNR across targets, a listener's ANL and preferred SNR for one target predicts their ANL and preferred SNR, respectively, for a different target. The lack of correlation between ANL and preferred SNR suggests different mechanisms underly these listener-preference metrics.

The article discusses practices of creating sound maps. I refer to these practices as cartographic and social. The starting point are internet sound maps for listening. In the article, I define them as audiovisual, digital representations that are created collaboratively based on the user-generated content mechanism and have internet interfaces based on global maps such as Google Maps or OpenStreetMap. The main aim of the article is to look at soundmapping practices in the context of shaping audiogeographic imagination and transphonic and locative listening. The main perspective of my considerations is the anthropologized perspective of critical, unrepresentative cartography. I show the development of sound maps that interest me, define their specificity and indicate conceptual problems related to the representation of the experience of a soundscape in the medium of a sound map.

Bicycles have become one of the major modes of urban public travel. Moreover, the high number of bicycle accidents has created challenges for road traffic safety. In addition, a relationship exists between single-handed use of handlebars and the use of mobile phones while cycling, which increases the risk of cycling. The current study considers not only the impact of mobile phone use while cycling on the safety of cyclists but also the impact of the mental load of a distracting task on cycling safety. Thirty-two college students were recruited to participate in a cycling experiment with distractions that was carried out on both campus and off-campus roads. A paired t-test was used to analyze cyclists' performance under different distraction states. The results show that when performing distracting tasks, the speed of cycling is significantly reduced, the acceleration and rate of change in the deflection angle are significantly increased, and the saccade frequency is significantly reduced. In addition, texting while cycling has a greater negative impact on cycling performance than does calling or listening to music. This study reveals the harm of mobile phone use while cycling by analyzing the effects and characteristics of manipulation performance and visual strategy. The study results can contribute to improving cyclist awareness regarding distraction safety and reducing the occurrence of mobile phone distraction issues. Furthermore, this approach provides a theoretical research basis for scientific and effective intervention and improvement measures in the future.

This paper describes the Design and Implementation of ICT-based E-learning programs that can be used for learning either through computer networks or personal computers (PCs) with CD ROMs. This research includes software engineering with methods of analysis, design, coding and evaluation. Based on the results of the study, it was obtained that the Design and Implementation of E-learning Program consisted of modules of material presentation, demo, analysis, examination, Practice test, Problem Answer and Help Module. The module can function as a learning tool that facilitates learning needs for learning activities that include reading, staring, watching, peeping, observing, simulating, demonstrating, answering questions, entering data, listening, tracing teaching materials, working on problems, and finding answers to problems.

Students especially at universities undergo a lot of pressure and stress, and mental health is something that must not be taken lightly, especially at the time of pandemic as we are experiencing now. The need for us to look into the mental health is constantly reminded everywhere. There are a lot of ways to reduce stress such as meditation, getting involved in sports and one of the most practiced methods is by listening to music. Music has been indeed proved to have positive effects on humans and that it aids healing process such as binaural beats and Solfeggio frequency. These frequencies of music have impact towards the brainwave. This study reports on how the design thinking process was used to better identify the most suitable means on integrating mobile Brain-Computer Interface (BCI) as an application to know the impacts of different type of frequencies of music on the human brain to reduce stress. Besides suggesting a generic guideline to develop an application for mobile BCI, this study also provides us insights into the readiness of mobile BCI as an application for common usage.

Autonomous learning is what college students need to learn. For some students who have difficulties in self-study, this paper develops a cloud computing intelligent optimization algorithm in the multimedia teaching mode of college students’ English autonomous learning system. This platform can help self-learners learn translation, listening, speaking, and other skills. At the same time, the platform also includes a self-assessment system including diagnostic evaluation, formative evaluation, and summative evaluation. The final test shows that the recognition rate of the system can reach more than 95%, which can meet the needs of use.

Information technology has become an important carrier for the implementation of flipped classrooms, giving full play to the role of modern education technology and transforming the traditional classroom teaching form into an important form in today’s education reform. This article mainly studies the simulation of the flipped classroom model of listening and speaking teaching for English majors based on artificial intelligence. A total of 31 English majors were selected for the experiment, including 7 boys and 24 girls. After obtaining the consent of school leaders and teachers, 16 weeks of experimental teaching were carried out in the class. The experimental subjects had not been exposed to the flipped classroom teaching mode before the start of the experiment, and they were willing to participate in this experiment. After the phased flipped classroom teaching mode was carried out in the experimental class, the teaching objects in the experimental class were selected in the form of interview questionnaires to understand students’ attitudes and acceptance of the flipped classroom teaching mode in English listening and speaking classes. Adjust and improve the poststage flipped classroom teaching model to provide reference. During the experiment, the control class adopted the multimedia courseware-assisted oral teaching method and, in the experimental class, flipped the classroom oral teaching mode. Teachers integrate resources when preparing lessons to design flip-flop classroom oral English teaching, and teachers guide students to use the internet to search for resources and preview independently. The average value of the English self-management learning ability of the students of this major was 27.48 points before the experiment started. After passing the experiment, the score increased to 38.90 points, and the average value increased significantly (t = −20.189, P < 0.01 ). A comparatively complete comparison of English listening and speaking teaching results was carried out. Before the implementation of the flipped classroom, the students’ average professional English score was 76.23 points, and the average score after the launch was 84.39 points. The overall average score increased by 8.16 points. With regard to students’ English learning scores, there has been some improvement. The results show that, through the implementation of flipped classrooms, students have exercised their ability to self-manage their learning and strengthened their ability to make learning plans, implement them, and reflect on the effectiveness of learning.

This research discusses the formation and use of babigo slang language by taking data sources on video footage of Saitou Kyouko as a babigo speaker on the Saitou Kyouko YouTube channel. The purpose of this study was to determine the formation and function of babigo. The method used in this research is qualitative method. A qualitative method is a research procedure that produces descriptive data in the form of words or speech of people and behaviors that can be observed. The data collection method uses the listening method, which is the method used to obtain data by listening to the use of the language. While the data analysis method uses descriptive methods which are research methods carried out based on the existing facts, so the results are describing the elements of language as it is. Based on the analysis of this research data, the author found 1) Babigo formation that inserts a single vocal syllable in each Japanese language syllable 2) The Function of babigo is used by young people as a secret language and can also be used as language games.

Obstacle avoidance is a major challenge during independent mobility for blind or visually impaired (BVI) people. Typically, BVI people can only perceive obstacles at a short distance (about 1 m, in case they are using the white cane), and some obstacles are hard to detect (e.g . , those elevated from the ground), or should not be hit by the white cane (e.g . , a standing person). A solution to these problems can be found in recent computer-vision techniques that can run on mobile and wearable devices to detect obstacles at a distance. However, in addition to detecting obstacles, it is also necessary to convey information about them in real time. This contribution presents WatchOut , a sonification technique for conveying real-time information about the main properties of an obstacle to a BVI person, who can then use this additional feedback to safely navigate in the environment. WatchOut was designed with a user-centered approach, involving four iterations of online listening tests with BVI participants in order to define, improve and evaluate the sonification technique, eventually obtaining an almost perfect recognition accuracy. WatchOut was also implemented and tested as a module of a mobile app that detects obstacles using state-of-the-art computer vision technology. Results show that the system is considered usable and can guide the users to avoid more than 85% of the obstacles.

In this paper, we propose a brain–computer interface (BCI)-based Human-in-the-Loop (Hit-Loop) agent for human and artificial intelligence (AI) colearning in music listening and appreciation with an Artificial Intelligence of Things (AIoT) application. The novel BCI-based Hit-Loop agent contains human intelligence with BCI-based AIoT-Fuzzy Markup Language (FML) and BCI-FML agents, as well as machine intelligence with AI-FML Hit-Loop and AIoT-FML agents. We used FML to facilitate communication between humans and the AI-FML robots through an AIoT-FML Learning Tool (AIoT-FML-LT), which was the core technology of the AI-FML Hit-Loop agent for the BCI-based music listening and appreciation application. Furthermore, the novel AIoT-FML-LT in conjunction with the BCI-FML and AIoT-FML agents was developed and presented for music listening and student learning. Moreover, the BCI-based AIoT-FML and AI-FML Hit-Loop agents were applied in English language learning, and the AIoT-FML-LT assisted in measuring student learning performance in Taiwan and Japan. The human-like high-level knowledge base and rule base were constructed by various domain experts, as well as the personalized electroencephalography (EEG) and student English learning data sets collected using the BCI device and AIoT-FML-LT, respectively, and were applied to machine learning models such as deep learning and particle swarm optimization. Additionally, the AIoT-FML-LT was connected to the AIoT-FML Hit-Loop agent for human and robot colearning. The relationship between human perceptions and the AIoT-FML Hit-Loop agent in terms of eyes, ears, nose, tongue, body, and brain corresponding to sights, sounds, smells, tastes, objects of touch, and mind are discussed. Finally, the students learned human language and AI language by using the AI-FML robots and AIoT-FML-LT together with the human English learning and AI-FML machine learning models, respectively. The experimental results reveal that the BCI-based Hit-Loop agent for human and AI-FML robot colearning in conjunction with AIoT applications can effectively facilitate music listening and appreciation application as well as English listening in Taiwan and Japan. The learning behavior and performance of the students also improved after incorporation of the human and robot colearning model.

This research aimed to get a clear picture of the application of these media in the learning process of Arabic teaching, especially Maharah Istima’ at the University of Darussalam Gontor. Learning Arabic for non-native speakers requires more variety and innovation, including in the selection of teaching materials, learning methods, and learning media. This is because learning Arabic in Indonesia is an on-going process in search for the ideal form of learning. Researchers used descriptive qualitative research methods with participatory observation data retrieval methods and documents. The researchers used Miles and Huberman’s technique applied resting upon the stages of data collection, data reduction, data presentation, and drawing conclusions. The results of the study indicated that the implementation of learning.aljazeera.net media in listening session rested upon 3 main stages. They were 1) pre-listening which contained vocabulary delivery, 2) the listening process which used videos from the learning.aljazeera.net website which were repeated at least 4 times, and then 3) the post-listening stage, covering the evaluation of listening activities from the simplest form to the most complex form of evaluation. The learning process of listening using the learning.aljazeera.net was a type of intensive, creative, concentrative, and explorative listening activity.

Listening is a powerful, under-appreciated, mechanism for acquiring information about the world. Changes in the hum of a car engine can offer an early clue about potential problems; the rustle of leaves informs us of weather conditions even before checking the forecast; slight fluctuations in the sound of a kettle tell us the water is almost boiled. These are but a few examples of important sonic interactions we have with our environment throughout the day.

Ecological momentary assessment (EMA) outcome measures can relate people's subjective auditory experience to their objective acoustical reality. While highly realistic, EMA data often contain considerable variability, such that it can be difficult to interpret the results with respect to differences in people's hearing ability. To address this challenge, a method for “guided” EMA is proposed and evaluated. Accompanied and instructed by a guide, normal-hearing participants carried out specific passive and active listening tasks inside a real-world public lunch scenario and answered EMA questionnaires related to aspects of spatial hearing, listening ability, quality, and effort. In situ speech and background noise levels were tracked, allowing the guided EMA task to be repeated inside two acoustically matched, loudspeaker-based laboratory environments: a 64-channel virtual sound environment (VSE) and a three-channel audiology clinic setup. Results showed that guided EMA provided consistent passive listening assessments across participants and conditions. During active listening, the clinic setup was found to be less challenging than the real-world and the VSE conditions. The proposed guided EMA approach may provide more focused real-world assessments and can be applied in realistic laboratory settings to aid the development of ecologically valid hearing testing.

What is “good” qualitative research? Considerable literature articulates criteria for quality in qualitative research. Common to all these criteria is the understanding that the data gathering process, often interviews, is central in assessing research quality. Studies have highlighted the preparation of the interview guide, appropriate ways to ask questions, and especially the interaction between interviewer and interviewee. To a lesser extent, qualitative scholars mention the importance of the interviewer’s listening abilities in obtaining the interviewee’s cooperation. Based on results of listening studies in the fields of psychology and organizational behavior, we argue that good listening is crucial for assessing the quality of qualitative research, yet remains a blind spot in qualitative data gathering. Drawing on our experience as qualitative researcher and listening researcher, we present practices for enhancing good listening in qualitative research, thereby enabling researchers to calibrate themselves as research instruments and obtain richer data.

The introduction of educational distance services into the training system is of urgent importance at the present time. This article presents a web platform for foreign students studying Russian (levels A1, A2). The methodology, approaches and methods of teaching Russian as a foreign language are listed. The obtained practical experience shows that learning through the use of digital technologies allows integrating educational technologies and increasing its effectiveness in synchronous and asynchronous learning. The research is based on the existing models in linguistic science, but at the same time its specificity and approach have no analogues in Kazakh science. The appearance of such a website will allow foreigners to improve their basic writing, speaking, reading and listening skills under the guidance of a teacher and independently. The use of digital services in the classroom contributes to the presentation of the material in a more accessible, visual form, which will help to improve the quality of memorization and academic performance in a mixed learning format.

Una de las metas a lograr en el campo clínico de la audioprótesis es alcanzar un nivel de escucha del habla cómodo e inteligible. Para lograr este objetivo se han creado señales de referencia basadas en el promedio del espectro del habla con las que poder evaluar la ganancia del los audífonos. El sexo, la edad, la localización del micrófono o la lengua son variables que influyen en el registro de esta medida. En este artículo revisamos la influencia de estos factores y presentamos las posibles aplicaciones audiológicas y audioprotésicas de este tipo de señales.

Purpose Listeners shift their listening strategies between lower level acoustic information and higher level semantic information to prioritize maximum speech intelligibility in challenging listening conditions. Although increasing task demands via acoustic degradation modulates lexical-semantic processing, the neural mechanisms underlying different listening strategies are unclear. The current study examined the extent to which encoding of lower level acoustic cues is modulated by task demand and associations with lexical-semantic processes. Method Electroencephalography was acquired while participants listened to sentences in the presence of four-talker babble that contained either higher or lower probability final words. Task difficulty was modulated by time available to process responses. Cortical tracking of speech—neural correlates of acoustic temporal envelope processing—were estimated using temporal response functions. Results Task difficulty did not affect cortical tracking of temporal envelope of speech under challenging listening conditions. Neural indices of lexical-semantic processing (N400 amplitudes) were larger with increased task difficulty. No correlations were observed between the cortical tracking of temporal envelope of speech and lexical-semantic processes, even after controlling for the effect of individualized signal-to-noise ratios. Conclusions Cortical tracking of the temporal envelope of speech and semantic processing are differentially influenced by task difficulty. While increased task demands modulated higher level semantic processing, cortical tracking of the temporal envelope of speech may be influenced by task difficulty primarily when the demand is manipulated in terms of acoustic properties of the stimulus, consistent with an emerging perspective in speech perception.

Purpose The acoustic source that is attended to by the listener in a mixture can be identified with a certain accuracy on the basis of their neural response recorded during listening, and various phenomena may be used to detect attention. For example, neural tracking (NT) and alpha power lateralization (APL) may be utilized in order to obtain information concerning attention. However, these methods of auditory attention detection (AAD) are typically tested in different experimental setups, which makes it impossible to compare their accuracy. The aim of this study is to compare the accuracy of AAD based on NT, APL, and their combination for a dichotic natural speech listening task. Method Thirteen adult listeners were presented with dichotic speech stimuli and instructed to attend to one of them. Electroencephalogram of the subjects was continuously recorded during the experiment using a set of 32 active electrodes. The accuracy of AAD was evaluated for trial lengths of 50, 25, and 12.5 s. AAD was tested for various parameters of NT- and APL-based modules. Results The obtained results suggest that NT of natural running speech provides similar accuracy to APL. The statistically significant improvement of the accuracy of AAD using a combined method has been observed not only for the longest duration of test samples (50 s, p = .005) but also for shorter ones (25 s, p = .011). Conclusions It seems that the combination of standard NT and APL significantly increases the effectiveness of accurate identification of the traced signal perceived by a listener under dichotic conditions. It has been demonstrated that, under certain conditions, the combination of NT and APL may provide a benefit for AAD in cocktail party scenarios.

Psychoacoustic parameters are mostly used for determining the sound quality of mechanical sounds. They have also started to be used for evaluating soundscapes of open areas and enclosed spaces. This research aims to find out the relationship between the psychoacoustic parameters and acoustic comfort in non-acoustic enclosed public spaces, specifically in eating establishments. Both on-site measurements and laboratory listening tests were conducted for five eating establishments. During on-site measurements, a simultaneous questionnaire study was also carried out. Subjective and objective data were comparatively evaluated. Listening tests were based on auralizations with calibrated simulation models. This provided a research model, that allows control over the acoustic environment without having to make real changes in the physical elements of the eating establishments. The auralization sound files were presented to listening test participants with headphones and they evaluated soundscapes with different psychoacoustic properties. As results, better acoustic comfort was found to be related with higher sharpness, lower reverberation time, lower loudness and lower roughness values for the examined parameter ranges.

Currently the trend of listening to music using music application is gaining popularity. One of the most popular streaming music applications is Spotify. Spotify’s strong brand image makes it easy for the public to remember. Spotify’s brand image was formed through electronic word of mouth (eWOM). eWOM is marketing communication strategy that influences and generates buying interest when users see interesting and positive comments on the internet regarding the Spotify streaming music application product. This study aimsto determine the effect of eWOM and brand image on interest in purchasing Spotify premium services. What is meant by premium service is a paid feature to get full and ad free access. This study uses a quantitative approach using a survey method which conducted on 100 students of STIKOM Interstudi who are users of the Spotify streaming music application. The results show that eWOM and brand image have a positive influence on the interest in buying premium services for the Spotify streaming music application

In order to strengthen the management of English classroom discipline and improve the efficiency of students’ English classroom learning, students’ English classroom behavior based on intelligent image recognition is analyzed in IoT (Internet of things). The working scenes and practical significance of deep learning and IoT are analyzed and then the effects of four models on students' behavior analysis in English classroom are discussed. The results show that the classroom behavior analysis model proposed is feasible. The recognition system judges whether the students are listening seriously from three aspects, namely students' side face, head up and down, and their eyelid opening. The comparison of the four models of VGG16, ResNet18, ResNet50 and AlexNet shows that the accurate recognition rate of VGG16 for students' behavior in English classroom reaches 94.15%. Experiments show that the method provides a more objective evaluation of students’ classroom behavior. As a whole, students’ classroom behavior analysis based on IIRT (intelligent image recognition technology) in IOT is practicable for improving English classroom efficiency.

This contribution researches various translation dictionaries – mobile apps with Latvian that are available for free on the Google Play Store and sets out the experience of developing specialised translation dictionaries at Ventspils University of Applied Sciences (VUAS). The results encounter three different dictionary models that are compiled for the acquisition of Languages for Special Purposes (LSPs) to support vocabulary (term) learning, translation, reading, listening and writing skills during the foreign language-learning process. Dictionaries with grammatical information and rich, supportive data or encyclopaedic information added are more useful for understanding specific domain concepts and content, as well as for the translation practice. Thus, the dictionary user does not have to look for another application or other source to get this additional information. Each new app takes up some space on the user’s smart device; therefore, it is important to maximise the support provided by a single dictionary. The research provides insight into the range of dictionaries with Latvian available on the Google Play Store, gives a brief description, and explains the practical experience of VUAS when working on developing such dictionaries and applying them for language-learning purposes and specialised terminology courses for students. The study also provides an overview of challenges, problems and lexicographic and information technology solutions for mobile applications. The goal of the research is analysing and characterising those free-of-charge dictionaries in the form of mobile apps with Latvian as one of the translation languages. Surveys were used to gather and analyse research data. A survey of dictionary users and a survey of language learners using dictionary apps were carried out after the development of the specialised dictionaries.

The goal of pronunciation teaching should be to enable learners to develop intelligible pronunciation and, in order to do this, it is important to teach perception and production of the most relevant segmental and suprasegmental features of pronunciation, considering specific groups of learners (CELCE-MURCIA et al. , 2010). Technology has played an important role in pronunciation teaching, and the applications developed for pronunciation instruction enable learners not only to engage in pronunciation activities, but to have access to a greater variety of input and immediate feedback. Having this in mind, this study aimed at analyzing the content, the pronunciation teaching steps, the features, and usability resources of pronunciation apps. In order to guide the analysis, a framework was developed based on literature related to the areas of pronunciation teaching and of Mobile Assisted Language Learning (MALL). The results showed that there is a tendency for the apps analyzed to focus more on segmentals. All of them offer description and analysis, listening discrimination, and controlled practice of the pronunciation features, as well as feedback. However, they were limited in terms of guided and communicative practice, of Automated Speech Recognition (ASR), and of variety of input.

A requirement for modern hearing aids is to evaluate a listening environment for the user and automatically apply appropriate gain and feature settings for optimal hearing in that listening environment. This has been predominantly achieved by the hearing aids' acoustic sensors, which measure acoustic characteristics such as the amplitude and modulation of the incoming sound sources. However, acoustic information alone is not always sufficient for providing a clear indication of the soundscape and user's listening needs. User activity such as being stationary or being in motion can drastically change these listening needs. Recently, hearing aids have begun utilizing integrated motion sensors to provide further information to the hearing aid's decision-making process when determining the listening environment. Specifically, accelerometer technology has proven to be an appropriate solution for motion sensor integration in hearing aids. Recent investigations have shown benefits with integrated motion sensors for both laboratory and real-world ecological momentary assessment measurements. The combination of acoustic and motion sensors provides the hearing aids with data to better optimize the hearing aid features in anticipation of the hearing aid user's listening needs.

The Ability to pronounce correct phoneme is one of the basic elements in learning Arabic. This research aimed to obtain data about Arabic pronunciation errors in students at junior high school. The research approach is qualitative. The sample of this study was 30 students of the Al-Mahmud IT Junior High School in Bima, Indonesia which were selected by using a simple random sample technique. The data of this research were the results of the students' audiovisual recordings when reciting Arabic text. Data analysis used the analysis model of Tarigan's, namely; data collection, identification, description, explanation, classification and evaluation. The results of this study The causes of phoneme errors are: environment, interference and performance. Classification of errors consist of omission, adding mad and Substitution. Evaluation and suggestion; there some ways to develop in learning Arabic phoneme are: continuous practice, listening to Arabic audio and videos.

As consumer’s consumption of digital media and usage of social media platforms continues to grow, businesses have been searching for new ways to track, analyze, and measure this online consumer behavior to support a diverse set of business functions such as marketing, customer support, and customer experience management. Given the heightened interest in social media analytics in the business community, educational institutions have also been interested in identifying new ways to introduce emerging social media analytics concepts and practices to their students. One category of social technologies that is specifically used to perform this type of analysis is social listening platforms. Brandwatch (recently acquired by Cision Ltd.) has created one of the leading social listening platforms. They also offer a version of this platform for educators named Brandwatch for Classrooms. This article provides an overview of the key features and capabilities of the Brandwatch for Classrooms platform, as well as numerous suggestions for incorporating the platform in marketing analytics undergraduate and graduate courses. The goal is to provide educators with practical information about social media analytics and guidance on successfully integrating social listening platforms in their course design.

In spring 2021, the authors of this article ran staff focus groups to explore stories about the impact of COVID-19 on the librarians who work at Malmö university library. In what way have their work tasks and the work situation changed during the COVID-19 pandemic? What have they learned from the experiences and what do they want to develop after the pandemic? Three strong themes emerged: the digital shift, remote working and the library as place. The findings were that the pandemic has created a reality of remote working and learning in higher education. In designing the future library we need to capitalise on the gains of the digitalisation as well as address the potential issues, including loneliness, isolation and motivation.

This correlational study of 433 adults (260 college students and 173 Mechanical-Turk workers) examined how the selfreported functions and experienced phenomenology of habitual inner speech (action guidance, problem solving/search, memory/attention regulation, emotion regulation, evaluate/motivate, other voices, inner dialogue, condensed speech) relate to self-awareness (self-reflection and controlled sense-of-self in the moment), potentially influence high-level aspects of self-regulation (self-preoccupation, self-compassion, wisdom, and the moral foundations of individualizing and binding), and psychological wellbeing. Hierarchical regression analysis revealed partial and mutual mediation between inner speech variables and self-awareness variables. Self-awareness was more consistently associated with self-regulation. The only inner speech variables associated with self-regulation in a beneficial way were memory/attention regulation (for wisdom about the self and the individualizing moral foundation) and evaluate/motivate (for the binding moral foundation). These findings suggest that, with the exception just described, inner speech (with the present dependent variables, and in adults) is easiest understood as an epiphenomenon.

OBJECTIVE Conventional directional hearing aid microphone technology may obstruct listening intentions when the talker and listener walk side by side. The purpose of the current study was to evaluate hearing aids that use a motion sensor to address listening needs during walking. DESIGN Each participant completed two walks in randomised order, one walk with each of two hearing aid programs: (1) conventional beamformer adaptation that activated an adaptive, multiband beamformer in loud environments and (2) motion-based beamformer adaptation that activated a pinna-mimicking microphone setting when walking was detected. Participants walked along a pre-defined track and completed tasks assessing speech understanding and environmental awareness. STUDY SAMPLE Participants were 22 older adults with moderate-to-severe hearing loss and experience using hearing aids. RESULTS More participants preferred the motion-based than conventional beamformer adaptation for speech understanding, environmental awareness, overall listening, and sound quality (p

Introducción. el artículo analiza los principios fundamentales de un nuevo enfoque de la organización de la educación, que ha recibido la definición de “aprendizaje conectado” en la literatura metodológica en lengua inglesa; las principales ventajas de la inclusión activa de los teléfonos inteligentes en la educación; algunas técnicas de uso del mensajero de WhatsApp en el aula para el inglés como lengua extranjera. Objetivo. Contribuir al uso de los recursos de Internet en la enseñanza del inglés para el desarrollo de las habilidades auditivas. Metodología. Se presta especial atención al aspecto de mejorar las habilidades auditivas de los estudiantes de facultades no lingüísticas, mediante el anàlisis de la experiencia en la organización del trabajo independiente para el aprendizaje del idioma Inglés como lengua extranjera en los distintos niveles de formación. Resultados. Las tecnologías informáticas utilizadas como parte de las ayudas didácticas electrónicas está disponible en la red local y parte en Internet. El desarrollo de entornos virtuales de aprendizaje ha optimizado la tarea de compartir recursos y evaluarlos de manera síncrona permitiendo que el estudiante pueda acceder de manera asíncrona desde cualquier ubicación física. Conclusiones. Los recursos de Internet son un medio eficaz para organizar el espacio educativo, mantener las relaciones sociales, ya que permiten a los participantes en el proceso de aprendizaje realizar actividades conjuntas, utilizar los últimos materiales en diversos formatos y también capacitar diversos tipos de actividades de habla. Se puede realizar un entrenamiento auténtico con el material de audio, resulta interesante, informativo y significativo, comprensible, en correspondència con la realidad moderna de una sociedad en evolución.

Directionality is the only hearing aid technology — in addition to amplification — proven to help hearing aid users hear better in noise. Hearing aid directionality has been documented to improve speech intelligibility in multiple laboratory studies. In contrast, real-world studies have shown a disconnect between the potential of the technology and what hearing aid users experience in their daily life. This article describes the real-world studies that inspired ReSound to take a different approach to applying directional microphone technology. This approach is based on the idea that hearing aid directionality can leverage natural binaural hearing and inherent listening strategies. The directional strategy includes three listening modes that will be explained. These are the Spatial Cue Preservation mode, the Binaural Listening mode, and the Speech Intelligibility mode. The strategy and the advantages it provides in terms of sound quality, spatial hearing, and improved signal-to-noise ratio with maintained awareness of surroundings are explained.

Hearing aid gain and signal processing are based on assumptions about the average user in the average listening environment, but problems may arise when the individual hearing aid user differs from these assumptions in general or specific ways. This article describes how an artificial intelligence (AI) mechanism that operates continuously on input from the user may alleviate such problems by using a type of machine learning known as Bayesian optimization. The basic AI mechanism is described, and studies showing its effects both in the laboratory and in the field are summarized. A crucial fact about the use of this AI is that it generates large amounts of user data that serve as input for scientific understanding as well as for the development of hearing aids and hearing care. Analyses of users' listening environments based on these data show the distribution of activities and intentions in situations where hearing is challenging. Finally, this article demonstrates how further AI-based analyses of the data can drive development.

Owing to the recent COVID-19 pandemic, people spend more time in their homes, and noise abatements between neighbouring units are increasing. Heavy/soft impact sounds are major noise sources in high-storey apartment buildings. Standardized heavy/soft impact sources are known to exhibit the most similar physical and subjective characteristics to real impact sounds such as a child running or jumping, and an adult walking. The single number quantity on a rubber ball has been standardized. Hence, a classification scheme for rubber-ball impact sounds also needs to be standardized. Several studies on subjective responses have been conducted on the rubber-ball impact sound in various situations. In this study, subjective responses on rubber and real impact sounds were compared. The subjective experiment was conducted in the listening chamber, which is furnished similarly to typical living rooms in Korean apartment buildings. In our experiment, the rubber-ball impact sounds recorded in the real apartment building and real impact sound recorded in the mock building were presented via a subwoofer and multi-channel loudspeaker system. Subjective responses were collected with a seven-point SD scale. © INTER-NOISE 2021 .All right reserved.

With its own unique musical vocabulary, soundscape composition has been unprecedently developed all over the world. The analysis of music pieces from the perspective of acoustics has its own advantages that cannot be replaced by the traditional musicology. Based on a music piece named Daybreaking for a hope to overcome COVID-19, this paper analyzes the acoustic elements of SPL, frequency spectrum and waveform. Combined with the traditional musicology, it can show that the undulation of waveform has associated with the formal form and the visualization of music. The frequency spectrum shows that, as the climax of the piece, the sound has a high energy in the range of 300-1000Hz, which is one of the extremely sensitive frequency ranges for human. The composer not only skillfully integrates the chanting of monks with the complex environmental sounds, but also reflects the innovative composition skills. The change of SPL from weak to strong or contrarily is also in line with the emotional expression, that is, the core idea of moving from nature to nature. Since soundscape composition retains more information, it is easier to immerse the audience in the listening process and appreciate the beautiful hope rather than the difficulty of understanding it. In the analysis of soundscape composition, musicological analysis and acoustic analysis are indispensable. © INTER-NOISE 2021 .All right reserved.

Hearing aids continue to acquire increasingly sophisticated sound-processing features beyond basic amplification. On the one hand, these have the potential to add user benefit and allow for personalization. On the other hand, if such features are to benefit according to their potential, they require clinicians to be acquainted with both the underlying technologies and the specific fitting handles made available by the individual hearing aid manufacturers. Ensuring benefit from hearing aids in typical daily listening environments requires that the hearing aids handle sounds that interfere with communication, generically referred to as “noise.” With this aim, considerable efforts from both academia and industry have led to increasingly advanced algorithms that handle noise, typically using the principles of directional processing and postfiltering. This article provides an overview of the techniques used for noise reduction in modern hearing aids. First, classical techniques are covered as they are used in modern hearing aids. The discussion then shifts to how deep learning, a subfield of artificial intelligence, provides a radically different way of solving the noise problem. Finally, the results of several experiments are used to showcase the benefits of recent algorithmic advances in terms of signal-to-noise ratio, speech intelligibility, selective attention, and listening effort.

As conversational agents and digital assistants become increasingly pervasive, understanding their synthetic speech becomes increasingly important. Simultaneously, speech synthesis is becoming more sophisticated and manipulable, providing the opportunity to optimize speech rate to save users time. However, little is known about people’s abilities to understand fast speech. In this work, we provide an extension of the first large-scale study on human listening rates, enlarging the prior study run with 453 participants to 1,409 participants and adding new analyses on this larger group. Run on LabintheWild, it used volunteer participants, was screen reader accessible, and measured listening rate by accuracy at answering questions spoken by a screen reader at various rates. Our results show that people who are visually impaired, who often rely on audio cues and access text aurally, generally have higher listening rates than sighted people. The findings also suggest a need to expand the range of rates available on personal devices. These results demonstrate the potential for users to learn to listen to faster rates, expanding the possibilities for human-conversational agent interaction.

This paper aims for identifying the type of codeswitching and codemixing found on the vlog and recognizing factors causing codeswitching and codemixing. The researchers take data from 14 vlogs of Nurul Taufik themed Jaamiah or Campus. Some steps done in doing the research including listening to the vlog, analysing, and reporting. The result shows that there are 20 data of codeswitching and 52 data of codemixing. All forms of codeswitching belong to extraneous in the form of sentences. Some factors causing code switching are including speakers, interlocutors, the presence of a third person and changes in the topic of discussion. In addition, the form of code mixing is extraneous codes in the form of words, phrases, baster, repetition of words, expressions, and clauses. The cause of codemixing is the speaker's desire to get the “right” expression, and the habits and relaxedness of the speech act participants in communicating.

Mobile edge computing was born in 2013 and is still in the process of technology R&D and industrialization. Although it is still in the early stages of development, as one of the core technologies of 5G, it has broad development prospects; this article aims to study the design and implementation of a mobile English teaching information service platform under edge computing, so as to improve the efficiency and level of English teaching. The implication of mobile English teaching is that English teachers and students use mobile devices for English teaching and communication at the same time. This article mainly focuses on the research of mobile English teaching information service platform based on edge computing. It discusses edge computing model and edge network architecture and is based on cognitive flexibility theory, informal learning theory, and situational cognition and learning theory. A mobile English teaching model based on “listening, reading, and listening” is constructed, and a mobile English teaching information service platform is designed and implemented. After investigation and research, it is concluded that this platform can improve the efficiency of teacher scheduling by 5%–6% and the student’s 2-3% course selection efficiency.

Huge competition in the market has made it difficult for enterprises to generate leads and acquire new customers. On the other hand, recent technological advancements have made accessing and disseminating information easy. Online social networking systems have been successful in capturing information about users and their social contacts. Such a network allows companies to effectively target the end customers due to intimate knowledge of their preferences and shared needs during social conversations. This paper presents a cloud-based social listening system that leverages such soft data to unfold lead generation. We demonstrate our approach using the Facebook data interface and carry out a socio-temporal analysis of the social interactions captured from customers' social networks and messages exchanged in that network. The proposed setup enables acquiring new customers effectively through referrals by utilizing online social channels.

This research proposes to evaluate and analyze the decision matrix for learner's English mobile applications (EMAs) based on multi-objective heuristic decision making with a view to listening, speaking, reading, and writing. Because of the number of criteria, the significance of parameters, and variance in results, EMAs are difficult. Decision making has built on the combination of listening, speaking, reading, and writing and EMA evaluation criteria for students. The requirements are adapted from a framework of pre-school education. Six alternatives and 17 skills as a requirement are included in decision-making results. The six EMA are then assessed, with six English learning experts distributing a review form. The application subsequently is evaluated using the best-worst method and preference-order technique (TOPSIS) using multi-objective heuristic decision making methods. The best-worst method is used to measure requirements, whereas TOPSIS is used to test and assess the applications. In two cases, namely person and group, TOPSIS is used. Internal and external aggregations are used throughout the group context. In effect, the aim of evaluating the proposed study and comparing it to six relative studies with scenarios and benchmarking checklists is to develop an objectives validation framework for e-apps.