Models for learning reverberant environments

Reverberation is present in all real life enclosures. From our workplaces to our homes and even in places designed as auditoria, such as concert halls and theatres. We have learned to understand speech in the presence of reverberation and also to use it for aesthetics in music. This thesis investigates novel ways enabling machines to learn the properties of reverberant acoustic environments. Training machines to classify rooms based on the effect of reverberation requires the use of data recorded in the room. The typical data for such measurements is the Acoustic Impulse Response (AIR) between the speaker and the receiver as a Finite Impulse Response (FIR) filter. Its representation however is high-dimensional and the measurements are small in number, which limits the design and performance of deep learning algorithms. Understanding properties of the rooms relies on the analysis of reflections that compose the AIRs and the decay and absorption of the sound energy in the room. This thesis proposes novel methods for representing the early reflections, which are strong and sparse in nature and depend on the position of the source and the receiver. The resulting representation significantly reduces the coefficients needed to represent the AIR and can be combined with a stochastic model from the literature to also represent the late reflections. The use of Finite Impulse Response (FIR) for the task of classifying rooms is investigated, which provides novel results in this field. The aforementioned issues related to AIRs are highlighted through the analysis. This leads to the proposal of a data augmentation method for the training of the classifiers based on Generative Adversarial Networks (GANs), which uses existing data to create artificial AIRs, as if they were measured in real rooms. The networks learn properties of the room in the space defined by the parameters of the low-dimensional representation that is proposed in this thesis.Open Acces

A Framework for Treating Model Uncertainty in the Asset Liability Management Problem

The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under the context of model uncertainty in the one-stage framework. In practice, uncertainty issues appear to several aspects of the problem, e.g. liability process characteristics, market conditions, inflation rates, inside information effects, etc. A framework relying on the notion of the Wasserstein barycenter is presented which is able to treat robustly this type of ambiguities by appropriate handling the various information sources (models) and appropriately reformulating the relevant decision making problem. The proposed framework can be applied to a number of different model settings leading to the selection of investment portfolios that remain robust to the various uncertainties appearing in the market. The paper is concluded with a numerical experiment for a static version of the ALM problem, employing standard modelling approaches, illustrating the capabilities of the proposed method with very satisfactory results in retrieving the true optimal strategy even in high noise cases.Comment: arXiv admin note: text overlap with arXiv:2207.0086

Discriminative feature domains for reverberant acoustic environments

Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment iden- tification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Ma- chine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Er- ror Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Ex- perimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications

Το ταφικό έθιμο της ανέγερσης τύμβου κατα μήκος της Ιονίας και Αδριατικής ακτής ως πολιτιστικό και κοινωνικό φαινόμενο

This article deals with the two-folded role of the burial custom of tumulus along the Adriatic and Ionian Arc, both as an impressive architectural construction that excels in the surrounding area, as well as a symbolic place of collective memory for the local communities. Initially, the main architectural features –the central burial, the soil and the enclosure– are presented, which, with varied local peculiarities, determine the emergence of the tumulus almost simultaneously in these regions and its evolution in the course of the 3rd and 2nd millennia BC. Furthermore, the article is focused on the chronological and geographical distribution of the tumulus custom starting from the northern Adriatic and ending in the southern part of the Ionian Sea with the scope of unfolding local similarities and differences. The social role of the tumulus as a labour-intensive, enduring and highly visible ancestral monument – signal (sema), is then addressed, via both its topographical correlation to its settlement and the surrounding landscape, and via the ceremonial acts performed in several of them in the study area. Finally, through the choice of selected groups of finds and the adoption of common burial practices, the cultural relations of these regions and the special bond developed between them during the Bronze Age are emerging

SOCIAL STORIES AND DIGITAL LITERACY PRACTICES FOR INCLUSIVE EDUCATION

This article deals with current issues of modern pedagogical practices incorporated in Special and Inclusive Education and touches upon Social Stories as a supplementary powerful learning tool especially in cases of children with autism and other similar communication deficits. According to our literature review, Social Stories better respond to the needs and abilities of the children personality regardless of their age by presenting a considerable amount of social information and best describing social schemata and situations. Also, they provide guidance for socially appropriate attitudes and behaviors, encouragement and support in learning and educational setting, both verbally and visually supported. To set the theoretical frame of this topic, an overview of social constructivism theory and the Unified Theory of Acceptance and Use of Technology are provided. Moreover, what is also under discussion in this article relates to the new digital challenges that have lately emerged after the combination of Social Stories with ICTs. Article visualizations

STUDENTS WITH SPECIAL EDUCATIONAL NEEDS IN GREEK HIGHER EDUCATION: ICTS AS A VITAL TOOL FOR INCLUSION

The present paper intends to report and analyze ongoing practices and policies with respect to the inclusion of Students with Special Educational Needs (SEN) and/or disabilities into Higher Education in Greece. To achieve this goal, the researchers systematically searched the current literature sources to find out the extent to and the ways in which European priorities set by article 24 of the Convention on the Rights of Inclusion of Persons with Special Educational Needs and/or Disabilities, have been advocated by Greek educational policy within the Higher Education context. Actually, the literature review demonstrates the existing law framework of the Greek national and local policy whose purpose is to promote the development and implementation of digitally assisted services which ought to take into consideration the needs of students with learning disabilities and comply with the international strides calling for a broader inclusive education. The results of this review showed that Greek universities have endeavored to respond successfully to the Greek legislation’s mandates and to fully address anti-discriminatory practice. However, more adjustments and decisive progress steps have to be made in relation to the curriculum and to teachers’ professional training to ensure all students’ inclusion.  Article visualizations

End-to-End Classification of Reverberant Rooms using DNNs

Reverberation is present in our workplaces, our homes and even in places designed as auditoria, such as concert halls and theatres. This work investigates how deep learning can use the effect of reverberation on speech to classify a recording in terms of the room in which it was recorded in. Approaches previously taken in the literature for the task relied on handpicked acoustic parameters as features used by classifiers. Estimating the values of these parameters from reverberant speech involves estimation errors, inevitably impacting the classification accuracy. This paper shows how DNNs can perform the classification in an end-to-end fashion, therefore by operating directly on reverberant speech. Based on the above, a method for the training of generalisable DNN classifiers and a DNN architecture for the task are proposed. A study is also made on the relationship between feature-maps derived by DNNs and acoustic parameters that describe known properties of reverberation. In the experiments shown, AIRs are used that were measured in 7 real rooms. The classification accuracy of DNNs is compared between the case of having access to the AIRs and the case of having access only to the reverberant speech recorded in the same rooms. The experiments show that with access to the AIRs a DNN achieves an accuracy of 99.1% and with access only to reverberant speech, the proposed DNN achieves an accuracy of 86.9%. The experiments replicate the testing procedure used in previous work, which relied on handpicked acoustic parameters, allowing the direct evaluation of the benefit of using deep learning.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processin

Statistical monitoring of functional data using the notion of Fr\'echet mean combined with the framework of the deformation models

The aim of this paper is to investigate possible advances obtained by the implementation of the framework of Fr\'echet mean and the generalized sense of mean that it offers, in the field of statistical process monitoring and control. In particular, the case of non-linear profiles which are described by data in functional form is considered and a framework combining the notion of Fr\'echet mean and deformation models is developed. The proposed monitoring approach is implemented to the intra-day air pollution monitoring task in the city of Athens where the capabilities and advantages of the method are illustrated.Comment: 31 pages, 11 figure

Clustering measure-valued data with Wasserstein barycenters

In this work, learning schemes for measure-valued data are proposed, i.e. data that their structure can be more efficiently represented as probability measures instead of points on $\R^d$, employing the concept of probability barycenters as defined with respect to the Wasserstein metric. Such type of learning approaches are highly appreciated in many fields where the observational/experimental error is significant (e.g. astronomy, biology, remote sensing, etc.) or the data nature is more complex and the traditional learning algorithms are not applicable or effective to treat them (e.g. network data, interval data, high frequency records, matrix data, etc.). Under this perspective, each observation is identified by an appropriate probability measure and the proposed statistical learning schemes rely on discrimination criteria that utilize the geometric structure of the space of probability measures through core techniques from the optimal transport theory. The discussed approaches are implemented in two real world applications: (a) clustering eurozone countries according to their observed government bond yield curves and (b) classifying the areas of a satellite image to certain land uses categories which is a standard task in remote sensing. In both case studies the results are particularly interesting and meaningful while the accuracy obtained is high.Comment: 18 pages, 3 figure