Learning Attention Mechanisms and Context: An Investigation into Vision and Emotion

Attention mechanisms for context modelling are becoming ubiquitous in neural architectures in machine learning. The attention mechanism is a technique that filters out information that is irrelevant to a given task and focuses on learning task-dependent fixation points or regions. Furthermore, attention mechanisms suggest a question about a given task, i.e. `what' to learn and `where/how' to learn for task-specific context modelling. The context is the conditional variables instrumental in deciding the categorical distribution for the given data. Also, why is learning task-specific context necessary? In order to answer these questions, context modelling with attention in the vision and emotion domains is explored in this thesis using attention mechanisms with different hierarchical structures. The three main goals of this thesis are building superior classifiers using attention-based deep neural networks~(DNNs), investigating the role of context modelling in the given tasks, and developing a framework for interpreting hierarchies and attention in deep attention networks. In the vision domain, gesture and posture recognition tasks in diverse environments, are chosen. In emotion, visual and speech emotion recognition tasks are chosen. These tasks are selected for their sequential properties for modelling a spatiotemporal context. One of the key challenges from a machine learning standpoint is to extract patterns which bear maximum correlation with the information encoded in its signal while being as insensitive as possible to other types of information carried by the signal. A possible way to overcome this problem is to learn task-dependent representations. In order to achieve that, novel spatiotemporal context modelling networks and the mixture of multi-view attention~(MOMA) networks are proposed using bidirectional long-short-term memory network (BLSTM), convolutional neural network~(CNN), Capsule and attention networks. A framework has been proposed to interpret the internal attention states with respect to the given task. The results of the classifiers in the assigned tasks are compared with the \textit{state-of-the-art} DNNs, and the proposed classifiers achieve superior results. The context in speech emotion recognition is explored deeply with the attention interpretation framework, and it shows that the proposed model can assign word importance based on acoustic context. Furthermore, it has been observed that the internal states of the attention bear correlation with human perception of acoustic cues for speech emotion recognition. Overall, the results demonstrate superior classifiers and context learning models with interpretable frameworks. The findings are very important for speech emotion recognition systems. In this thesis, not only better models are produced, but also the interpretability of those models are explored, and their internal states are analysed. The phones and words are aligned with the attention vectors, and it is seen that the vowel sounds are more important for defining emotion acoustic cues than the consonants, and the model can assign word importance based on acoustic context. Also, how these approaches for emotion recognition using word importance for predicting emotions are demonstrated by the attention weight visualisation over the words. In a broader perspective, the findings from the thesis about gesture, posture and emotion recognition may be helpful in tasks like human-robot interaction~(HRI) and conversational artificial agents (such as Siri, Alexa). The communication is grounded with the symbolic and sub-symbolic cues of intent either from visual, audio or haptics. The understanding of intent is much dependent on the reasoning about the situational context. Emotion, i.e.\ speech and visual emotion, provides context to a situation, and it is a deciding factor in the response generation. Emotional intelligence and information from vision, audio and other modalities are essential for making human-human and human-robot communication more natural and feedback-driven

Representation Analysis Methods to Model Context for Speech Technology

Speech technology has developed to levels equivalent with human parity through the use of deep neural networks. However, it is unclear how the learned dependencies within these networks can be attributed to metrics such as recognition performance. This research focuses on strategies to interpret and exploit these learned context dependencies to improve speech recognition models. Context dependency analysis had not yet been explored for speech recognition networks. In order to highlight and observe dependent representations within speech recognition models, a novel analysis framework is proposed. This analysis framework uses statistical correlation indexes to compute the coefficiency between neural representations. By comparing the coefficiency of neural representations between models using different approaches, it is possible to observe specific context dependencies within network layers. By providing insights on context dependencies it is then possible to adapt modelling approaches to become more computationally efficient and improve recognition performance. Here the performance of End-to-End speech recognition models are analysed, providing insights on the acoustic and language modelling context dependencies. The modelling approach for a speaker recognition task is adapted to exploit acoustic context dependencies and reach comparable performance with the state-of-the-art methods, reaching 2.89% equal error rate using the Voxceleb1 training and test sets with 50% of the parameters. Furthermore, empirical analysis of the role of acoustic context for speech emotion recognition modelling revealed that emotion cues are presented as a distributed event. These analyses and results for speech recognition applications aim to provide objective direction for future development of automatic speech recognition systems

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Predicting Humans’ Identity and Mental Load from EEG: Performed by AI

EEG-based brain machine/computer interfaces (BMIs/BCIs) have a wide range of clinical and non-clinical applications. Mental workload (MW) classification, emotion recognition, motor imagery, seizure detection, and sleep stage scoring are among the active BCI research areas. One of the relatively new BCI area is EEG-based human subject recognition (i.e., EEG biometric). There still exist several challenges that need to be addressed to design a successful EEG-based biometric model applicable for real-world environments. First, there is a need for a protocol that can elicit the individual dependent EEG responses in a short period of time. A classification algorithm with high generalization power is also required to deal with the EEG signals classification task. The latter is a common challenge for all EEG-based BCI paradigms; given the non-stationary nature of the EEG signals and the small size of the EEG datasets. In addition, to building a stable EEG biometric model, the effects of human mental states (e.g., emotion, mental load) on the model performance needs to be carefully examined. In this thesis, a new protocol for the area of the EEG biometric has been proposed. The proposed protocol called “(the) N-back task” is based on the human working memory and the experimental results obtained in this thesis prove that the EEG signals elicited by the N-back task contain subject specific features, even for very short time intervals. It has also been shown that three load levels of the typical N-back task are all capable of evoking subject specific EEG features. As a result, the N-back task can be used as a protocol having more than one mode (i.e, cancelable protocol) that comes with added security benefits. The EEG signals evoked by the N-back task have been used to train a compact convolutional neural network called the EEGNet. A configuration of the EEGNet having 16 temporal and 2 spatial filters has reached an identification accuracy of approximately 97% using data instances as short as 1.1s for a pool of 26 subjects. To further improve the accuracy, a novel ensemble classifier has been designed in this thesis. The principle underlying the proposed ensemble is the “division and exclusion” of the EEG channels guided by scalp locations. The ensemble classifier has (statistically significantly) improved the subject recognition rate from 97% to 99%. Performance of the proposed ensemble model has also been assessed in the EEG-based MW classification paradigm. The ensemble classifier outperformed the single EEGNet as well as a state-of-the-art classifier called WLnet in the challenging scenario of the subject-independent (cross-subject) MW classification. The results suggest that the ensemble structure proposed in this thesis can generalize to different BCI paradigms. Finally, effects of the mental workload on the performance of the EEG-based subject authentication models have been thoroughly explored in this thesis. The obtained results affirm that MW of the genuine and impostor subjects at the train and test phases have significant effects on both false negative rate (FNR) and false positive rate (FPR) of an authentication system. Different subjects have also shown different clusters of authentication behaviors when affected by the MW changes. This finding establishes the importance of the human’s mental load in the design of real-world EEG authentication systems and introduces a new investigation line for the EEG biometric community

Automatic Recognition of Non-Verbal Acoustic Communication Events With Neural Networks

Non-verbal acoustic communication is of high importance to humans and animals: Infants use the voice as a primary communication tool. Animals of all kinds employ acoustic communication, such as chimpanzees, which use pant-hoot vocalizations for long-distance communication. Many applications require the assessment of such communication for a variety of analysis goals. Computational systems can support these areas through automatization of the assessment process. This is of particular importance in monitoring scenarios over large spatial and time scales, which are infeasible to perform manually. Algorithms for sound recognition have traditionally been based on conventional machine learning approaches. In recent years, so-called representation learning approaches have gained increasing popularity. This particularly includes deep learning approaches that feed raw data to deep neural networks. However, there remain open challenges in applying these approaches to automatic recognition of non-verbal acoustic communication events, such as compensating for small data set sizes. The leading question of this thesis is: How can we apply deep learning more effectively to automatic recognition of non-verbal acoustic communication events? The target communication types were specifically (1) infant vocalizations and (2) chimpanzee long-distance calls. This thesis comprises four studies that investigated aspects of this question: Study (A) investigated the assessment of infant vocalizations by laypersons. The central goal was to derive an infant vocalization classification scheme based on the laypersons' perception. The study method was based on the Nijmegen Protocol, where participants rated vocalization recordings through various items, such as affective ratings and class labels. Results showed a strong association between valence ratings and class labels, which was used to derive a classification scheme. Study (B) was a comparative study on various neural network types for the automatic classification of infant vocalizations. The goal was to determine the best performing network type among the currently most prevailing ones, while considering the influence of their architectural configuration. Results showed that convolutional neural networks outperformed recurrent neural networks and that the choice of the frequency and time aggregation layer inside the network is the most important architectural choice. Study (C) was a detailed investigation on computer vision-like convolutional neural networks for infant vocalization classification. The goal was to determine the most important architectural properties for increasing classification performance. Results confirmed the importance of the aggregation layer and additionally identified the input size of the fully-connected layers and the accumulated receptive field to be of major importance. Study (D) was an investigation on compensating class imbalance for chimpanzee call detection in naturalistic long-term recordings. The goal was to determine which compensation method among a selected group improved performance the most for a deep learning system. Results showed that spectrogram denoising was most effective, while methods for compensating relative imbalance either retained or decreased performance.:1. Introduction 2. Foundations in Automatic Recognition of Acoustic Communication 3. State of Research 4. Study (A): Investigation of the Assessment of Infant Vocalizations by Laypersons 5. Study (B): Comparison of Neural Network Types for Automatic Classification of Infant Vocalizations 6. Study (C): Detailed Investigation of CNNs for Automatic Classification of Infant Vocalizations 7. Study (D): Compensating Class Imbalance for Acoustic Chimpanzee Detection With Convolutional Recurrent Neural Networks 8. Conclusion and Collected Discussion 9. AppendixNonverbale akustische Kommunikation ist für Menschen und Tiere von großer Bedeutung: Säuglinge nutzen die Stimme als primäres Kommunikationsmittel. Schimpanse verwenden sogenannte 'Pant-hoots' und Trommeln zur Kommunikation über weite Entfernungen. Viele Anwendungen erfordern die Beurteilung solcher Kommunikation für verschiedenste Analyseziele. Algorithmen können solche Bereiche durch die Automatisierung der Beurteilung unterstützen. Dies ist besonders wichtig beim Monitoring langer Zeitspannen oder großer Gebiete, welche manuell nicht durchführbar sind. Algorithmen zur Geräuscherkennung verwendeten bisher größtenteils konventionelle Ansätzen des maschinellen Lernens. In den letzten Jahren hat eine alternative Herangehensweise Popularität gewonnen, das sogenannte Representation Learning. Dazu gehört insbesondere Deep Learning, bei dem Rohdaten in tiefe neuronale Netze eingespeist werden. Jedoch gibt es bei der Anwendung dieser Ansätze auf die automatische Erkennung von nonverbaler akustischer Kommunikation ungelöste Herausforderungen, wie z.B. die Kompensation der relativ kleinen Datenmengen. Die Leitfrage dieser Arbeit ist: Wie können wir Deep Learning effektiver zur automatischen Erkennung nonverbaler akustischer Kommunikation verwenden? Diese Arbeit konzentriert sich speziell auf zwei Kommunikationsarten: (1) vokale Laute von Säuglingen (2) Langstreckenrufe von Schimpansen. Diese Arbeit umfasst vier Studien, welche Aspekte dieser Frage untersuchen: Studie (A) untersuchte die Beurteilung von Säuglingslauten durch Laien. Zentrales Ziel war die Ableitung eines Klassifikationsschemas für Säuglingslaute auf der Grundlage der Wahrnehmung von Laien. Die Untersuchungsmethode basierte auf dem sogenannten Nijmegen-Protokoll. Hier beurteilten die Teilnehmenden Lautaufnahmen von Säuglingen anhand verschiedener Variablen, wie z.B. affektive Bewertungen und Klassenbezeichnungen. Die Ergebnisse zeigten eine starke Assoziation zwischen Valenzbewertungen und Klassenbezeichnungen, die zur Ableitung eines Klassifikationsschemas verwendet wurde. Studie (B) war eine vergleichende Studie verschiedener Typen neuronaler Netzwerke für die automatische Klassifizierung von Säuglingslauten. Ziel war es, den leistungsfähigsten Netzwerktyp unter den momentan verbreitetsten Typen zu ermitteln. Hierbei wurde der Einfluss verschiedener architektonischer Konfigurationen innerhalb der Typen berücksichtigt. Die Ergebnisse zeigten, dass Convolutional Neural Networks eine höhere Performance als Recurrent Neural Networks erreichten. Außerdem wurde gezeigt, dass die Wahl der Frequenz- und Zeitaggregationsschicht die wichtigste architektonische Entscheidung ist. Studie (C) war eine detaillierte Untersuchung von Computer Vision-ähnlichen Convolutional Neural Networks für die Klassifizierung von Säuglingslauten. Ziel war es, die wichtigsten architektonischen Eigenschaften zur Steigerung der Erkennungsperformance zu bestimmen. Die Ergebnisse bestätigten die Bedeutung der Aggregationsschicht. Zusätzlich Eigenschaften, die als wichtig identifiziert wurden, waren die Eingangsgröße der vollständig verbundenen Schichten und das akkumulierte rezeptive Feld. Studie (D) war eine Untersuchung zur Kompensation der Klassenimbalance zur Erkennung von Schimpansenrufen in Langzeitaufnahmen. Ziel war es, herauszufinden, welche Kompensationsmethode aus einer Menge ausgewählter Methoden die Performance eines Deep Learning Systems am meisten verbessert. Die Ergebnisse zeigten, dass Spektrogrammentrauschen am effektivsten war, während Methoden zur Kompensation des relativen Ungleichgewichts die Performance entweder gleichhielten oder verringerten.:1. Introduction 2. Foundations in Automatic Recognition of Acoustic Communication 3. State of Research 4. Study (A): Investigation of the Assessment of Infant Vocalizations by Laypersons 5. Study (B): Comparison of Neural Network Types for Automatic Classification of Infant Vocalizations 6. Study (C): Detailed Investigation of CNNs for Automatic Classification of Infant Vocalizations 7. Study (D): Compensating Class Imbalance for Acoustic Chimpanzee Detection With Convolutional Recurrent Neural Networks 8. Conclusion and Collected Discussion 9. Appendi

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise