Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptions, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. © 2008 IEEE

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

DigiArt: towards a virtualization of Cultural Heritage

DigiArt is a Europe-wide project aimed at providing a new, cost efficient solution to the capture, processing and display of cultural artefacts. The project will change the ways in which the public interact with cultural objects and spaces in a dramatic way. This project is unique in its collaborative approach: cultural heritage professionals working directly with electrical, mechanical, optical and software engineers to develop a solution to current issues faced by the museum sector. The innovations created by the engineers are driven by the demand of the cultural heritage sector. The diversity of the objects and spaces of the three test museums are challenging the engineers to provide a tool useful for a broad variety of indoor and outdoor museums in the future. This goes from using Unmanned Aerial Vehicle (UAVs or drones) to fly and record large sites, to using scanners to record fine jewellery. As a case study, we present here the use-case of Scladina Cave. At the end of the project, the Scladina Cave Archaeological Centre will offer two different visitor experiences. The first uses virtual reality, which will be available anytime, anywhere, to anyone with an internet connected device. The second will use augmented reality technologies within the cave site. The augmented reality visit of the cave will enhance the tour of Scladina by offering visits that would not be possible where it not for the augmented reality, where 3D objects and animations will contribute to offer a new 3D-immersive experience

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the crosscorrelation and the magnitude of the corresponding the crosspower spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported

Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Uterine stump adenocarcinoma in a bitch with an ovarian remnant: A case report

Θηλυκός σκύλος, ηλικίας 3 ετών, προσκομίστηκε 1,5 χρόνο μετά από υποτιθέμενη στείρωση, διότι παρουσίαζε επί 2 μήνες αιμορραγικό έκκριμα από το αιδοίο. Στην κλινική εξέταση ψηλαφήθηκε ευμεγέθες, συμπαγές,ωοειδές μόρφωμα στην οπίσθια κοιλία, το οποίο στην απεικονιστική διερεύνηση προσδιορίστηκε ως διογκωμένο κολόβωμα μήτρας. Επιπλέον διαπιστώθηκε υψηλή συγκέντρωση προγεστερόνης στον ορό του αίματος, αποδεικτική παρουσίας λειτουργικού ωοθηκικού ιστού σε δίοιστρο. Με υποψία πυομήτρας του κολοβώματος της μήτρας  καιλιγότερο άλλου είδους μορφώματος πραγματοποιήθηκε μέση λαπαροτομή. Βρέθηκε μόρφωμα – μάζα στο πρόσθιο άκρο του κολοβώματος το οποίο εξαιρέθηκε μαζί με το υπόλοιπο σώμα της μήτρας, καθώς και υπόλειμμα δεξιάς ωοθήκης. Το μόρφωμα προσδιορίστηκε ιστοπαθολογικά ως αδενοκαρκίνωμα και διαπιστώθηκε για πρώτη φορά σε κολόβωμα μήτρας σκύλας. Η αποτυχημένη, μερική ωοθηκυστερεκτομή μπορεί να επιτρέψει ακόμη και την εξαλλαγή των ιστών του κολοβώματος της μήτρας.A 3-year-old female spayed dog was presented with a history of sanguineous vaginal discharge of 2 month duration. The dog was spayed 1.5 years before presentation. Clinical examination revealed a large, solid, ovoid mass in the caudal abdomen, recognized by diagnostic imaging as an enlargement at the top of the uterine stump. Additionally, high serum progesterone concentration was measured, confirming the presence of functional ovarian tissue in dioestrus. With a suspicion for a related uterine stump pyometra or less likely, for other enlargements, a cοeliotomy was performed. A mass at the apex of the uterine body and a right side ovarian remnant were found. Both structures and the remaining uterine stump were excised. The uterine remnant mass was histologically diagnosed as uterine adenocarcinoma, herein detected for the first time at the uterine stump in the bitch. Unsuccessful, incomplete ovariohysterectomy may permit even neoplastic transformation of uterine stump tissues

A serious games platform for cognitive rehabilitation with preliminary evaluation

In recent years Serious Games have evolved substantially, solving problems in diverse areas. In particular, in Cognitive Rehabilitation, Serious Games assume a relevant role. Traditional cognitive therapies are often considered repetitive and discouraging for patients and Serious Games can be used to create more dynamic rehabilitation processes, holding patients' attention throughout the process and motivating them during their road to recovery. This paper reviews Serious Games and user interfaces in rehabilitation area and details a Serious Games platform for Cognitive Rehabilitation that includes a set of features such as: natural and multimodal user interfaces and social features (competition, collaboration, and handicapping) which can contribute to augment the motivation of patients during the rehabilitation process. The web platform was tested with healthy subjects. Results of this preliminary evaluation show the motivation and the interest of the participants by playing the games.- This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia in the scope of the projects: PEst-UID/CEC/00319/2015 and PEst-UID/CEC/00027/2015. The authors would like to thank also all the volunteers that participated in the study