Drivers of Sustainable Entrepreneurial Intentions in the Case of Serbian Students

The present research aims to establish the antecedents of sustainable entrepreneurial intention, having as reference theoretical framework the model of entrepreneurial event and the model of planned behavior, integrated and adapted to the context of sustainable entrepreneurship. At the level of investigated population, consisting of 150 students from two Serbian universities, the empirical results emphasized that: personality traits and environmental values are significant and direct predictors of behavioral characteristics; entrepreneurial education and behavioral characteristics have an indirect influence on sustainable entrepreneurial intention being mediated by the desire and feasibility of sustainable entrepreneurship perceived by respondents. As theoretical utility of the research, the current study is among the few that tried to integrate and expand two competing models in order to establish the antecedents of sustainable entrepreneurial intent. The research model adopted variables specific for the two models and integrated personality traits, environmental values and entrepreneurial education in order to establish direct and indirect determinants of sustainable entrepreneurial intention. At practical level, the validation of the research model emphasizes the utility of stimulating youth’ sustainable entrepreneurial intention and applicability for future academic research endeavors. In order to stimulate sustainable entrepreneurial intentions, the validated research model indicates to governmental and university decision makers the need to implement programs promoting environmental values and integrating sustainability into the entrepreneurial education of youth

Engagement in Music Therapy: A Detailed study of Communication Between the Therapist and Client Presenting with Severe and Multiple Handicaps

The impact of multiple disabilities causes difficulties in the area of communication. Individuals with severe and multiple handicaps often have no verbal language as a result of serious physical impairments. This population may show little obvious response and it is therefore difficult to know if they are engaged and for the person him or herself to maintain engagement when involved in activities. The purpose of the study was to find out what happens in a normal music therapy session, during moments of perceived engagement. Four individuals experienced in the field of multiple disabilities were invited to take part in semi-structured interviews where they observed a half hour video of a therapist and a student with severe and multiple handicaps participating in music therapy. Data was analyzed in two steps, firstly through participants observing and explaining their reactions to video of music therapy and secondly by the researcher interviewing the participants and writing up a transcript of their commentaries about the video. The key themes that emerged in participants' descriptions of engagement during moments in music therapy suggest it is possible to observe and identify engagement as reflected in the students' non-verbal responses, such as body movement, eye contact and vocalizations

Self-Supervised Learning for Speech Enhancement through Synthesis

Modern speech enhancement (SE) networks typically implement noise suppression through time-frequency masking, latent representation masking, or discriminative signal prediction. In contrast, some recent works explore SE via generative speech synthesis, where the system's output is synthesized by a neural vocoder after an inherently lossy feature-denoising step. In this paper, we propose a denoising vocoder (DeVo) approach, where a vocoder accepts noisy representations and learns to directly synthesize clean speech. We leverage rich representations from self-supervised learning (SSL) speech models to discover relevant features. We conduct a candidate search across 15 potential SSL front-ends and subsequently train our vocoder adversarially with the best SSL configuration. Additionally, we demonstrate a causal version capable of running on streaming audio with 10ms latency and minimal performance degradation. Finally, we conduct both objective evaluations and subjective listening studies to show our system improves objective metrics and outperforms an existing state-of-the-art SE model subjectively

CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment

Speech quality assessment has been a critical component in many voice communication related applications such as telephony and online conferencing. Traditional intrusive speech quality assessment requires the clean reference of the degraded utterance to provide an accurate quality measurement. This requirement limits the usability of these methods in real-world scenarios. On the other hand, non-intrusive subjective measurement is the ``golden standard" in evaluating speech quality as human listeners can intrinsically evaluate the quality of any degraded speech with ease. In this paper, we propose a novel end-to-end model structure called Convolutional Context-Aware Transformer (CCAT) network to predict the mean opinion score (MOS) of human raters. We evaluate our model on three MOS-annotated datasets spanning multiple languages and distortion types and submit our results to the ConferencingSpeech 2022 Challenge. Our experiments show that CCAT provides promising MOS predictions compared to current state-of-art non-intrusive speech assessment models with average Pearson correlation coefficient (PCC) increasing from 0.530 to 0.697 and average RMSE decreasing from 0.768 to 0.570 compared to the baseline model on the challenge evaluation test set

Performance analysis of wireless communication system in general fading environment subjected to shadowing and interference

In this paper, performance analysis of wireless communication over α−η−μ fading channels has been investigated. First, analysis has been carried out for the case when communication is subjected to the influence of co-channel interference. Closed-form expressions have been derived for the probability density function and cumulative distribution function of the received signal-to-interference ratio. Outage probability has been obtained for this case, in the function of various values of system parameters, and also for the case when selection diversity has been presented at the reception. Further, simultaneous multipath fading and shadowing occurrence has been analyzed, through deriving novel composite Gamma long-time faded α−η−μ fading distribution. First-order statistical parameters have been obtained in closed form, for this novel composite distribution, and capitalizing on them, standard performance measures have been efficiently evaluated, graphically presented and discussed in the function of system parameters