40 research outputs found

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Unsupervised Learning Algorithm for Noise Suppression and Speech Enhancement Applications

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    Smart and intelligent devices are being integrated more and more into day-to-day life to perform a multitude of tasks. These tasks include, but are not limited to, job automation, smart utility management, etc., with the aim to improve quality of life and to make normal day-to-day chores as effortless as possible. These smart devices may or may not be connected to the internet to accomplish tasks. Additionally, human-machine interaction with such devices may be touch-screen based or based on voice commands. To understand and act upon received voice commands, these devices require to enhance and distinguish the (clean) speech signal from the recorded noisy signal (that is contaminated by interference and background noise). The enhanced speech signal is then analyzed locally or in cloud to extract the command. This speech enhancement task may effectively be achieved if the number of recording microphones is large. But incorporating many microphones is only possible in large and expensive devices. With multiple microphones present, the computational complexity of speech enhancement algorithms is high, along with its power consumption requirements. However, if the device under consideration is small with limited power and computational capabilities, having multiple microphones is not possible. For example, hearing aids and cochlear implant devices. Thus, most of these devices have been developed with a single microphone. As a result of this handicap, developing a speech enhancement algorithm for assisted learning devices with a single microphone, while keeping computational complexity and power consumption of the said algorithm low, is a challenging problem. There has been considerable research to solve this problem with good speech enhancement performance. However, most real-time speech enhancement algorithms lose their effectiveness if the level of noise present in the recorded speech is high. This dissertation deals with this problem, i.e., the objective is to develop a method that enhances performance by reducing the input signal noise level. To this end, it is proposed to include a pre-processing step before applying speech enhancement algorithms. This pre-processing performs noise suppression in the transformed domain by generating an approximation of the noisy signals’ short-time Fourier transform. The approximated signal with improved input signal to noise ratio is then used by other speech enhancement algorithms to recover the underlying clean signal. This approximation is performed by using the proposed Block-Principal Component Analysis (Block-PCA) algorithm. To illustrate efficacy of the methodology, a detailed performance analysis under multiple noise types and noise levels is followed, which demonstrates that the inclusion of the pre-processing step improves considerably the performance of speech enhancement algorithms when compared to other approaches with no pre-processing steps

    Age differences in conspiracy beliefs around Covid-19 pandemic and (dis)trust in the government

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    Objective: Times of societal crisis, such as the COVID-19 pandemic, during which people need to make sense of a chaotic world and to protect their health and lives, according to psychological research, represent suitable ground for the development of conspiracy theories about origins, spread, and treatment of the threat (coronavirus). Although numerous studies have been conducted on this issue since the beginning of the pandemic until today, most of the studies were conducted on the adult population with limited insights into development of the conspiracy beliefs in adolescence or over the lifespan. Objective of this study is precisely to explore how conspiracy beliefs regarding COVID-19 pandemic differentiate between multiple age groups (cross-sectional design), what are their sources and contexts, and how do they relate with the tendency to trust the government. Methodology: Data were gathered through eight focus group discussions with four age groups (11-12, 14-15, 18-19, 30+) in Serbia. Results: Based on critical discourse analysis, this paper identifies the differences in content and the sources of conspiracy thinking and how it relates to trust in the government. Study shows that high distrust in Serbian government is associated with conspiracy beliefs both within youth and adults. However, while among adolescents this finding is exclusively related with their beliefs that ruling structures have financial gain from the pandemic, against the interests of citizens, among adults it is related to the belief that the government (un)intentionally submits to the new global order that is managed by one or more powerful actors who are coordinated in secret action to achieve an outcome that is of public interest, but not public knowledge. Conclusion: The results will be discussed within current socio-political climate in Serbia, as well as the basis for understanding psychological factors which may underlie these tendencies in conspiracy theorizing, such as social identification, collective narcissism, authoritarianism, and social dominance orientation

    Mathematics and Digital Signal Processing

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    Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

    MIMO Systems

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    In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

    Pre-processing of Speech Signals for Robust Parameter Estimation

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    Model-based Analysis and Processing of Speech and Audio Signals

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    A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

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    When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available
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