Video summarisation: A conceptual framework and survey of the state of the art

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users

Is voice a marker for autism spectrum disorder? A systematic review and meta-analysis

Individuals with Autism Spectrum Disorder (ASD) tend to show distinctive, atypical acoustic patterns of speech. These behaviours affect social interactions and social development and could represent a non-invasive marker for ASD. We systematically reviewed the literature quantifying acoustic patterns in ASD. Search terms were: (prosody OR intonation OR inflection OR intensity OR pitch OR fundamental frequency OR speech rate OR voice quality OR acoustic) AND (autis* OR Asperger). Results were filtered to include only: empirical studies quantifying acoustic features of vocal production in ASD, with a sample size > 2, and the inclusion of a neurotypical comparison group and/or correlations between acoustic measures and severity of clinical features. We identified 34 articles, including 30 univariate studies and 15 multivariate machine-learning studies. We performed metaanalyses of the univariate studies, identifying significant differences in mean pitch and pitch range between individuals with ASD and comparison participants (Cohen’s d of 0.4-0.5 and discriminatory accuracy of about 61-64%). The multivariate studies reported higher accuracies than the univariate studies (63-96%). However, the methods used and the acoustic features investigated were too diverse for performing meta-analysis. We conclude that multivariate studies of acoustic patterns are a promising but yet unsystematic avenue for establishing ASD markers. We outline three recommendations for future studies: open data, open methods, and theory-driven research

Feature Extraction in Music information retrival using Machine Learning Algorithms

Music classification is essential for faster Music record recovery. Separating the ideal arrangement of highlights and selecting the best investigation technique are critical for obtaining the best results from sound grouping. The extraction of sound elements could be viewed as an exceptional case of information sound information being transformed into sound instances. Music division and order can provide a rich dataset for the analysis of sight and sound substances. Because of the great dimensionality of sound highlights as well as the variable length of sound fragments, Music layout is dependent on the overpowering computation. By focusing on rhythmic aspects of different songs, this article provides an introduction of some of the possibilities for computing music similarity. Almost every MIR toolkit includes a method for extracting the beats per minute (BPM) and consequently the tempo of each music. The simplest method of computing very low-level rhythmic similarities is to sort and compare songs solely by their tempo There are undoubtedly far better and more precise solutions.  work discusses some of the most promising ways for computing rhythm similarities in a Big Data framework usaing machine Learning algorithms

Feminist Stereotypes: Communal vs. Agentic

This study examined relationships between facial appearance, gender-linked traits, and feminist stereotypes. Naïve college students rated traits based on facial appearance of female CEO\u27s whose companies appeared in the Forbes 1000 list. The photos of each female CEO (n=35) were randomly combined with two descriptive identifiers; an occupation (n=9) and an interest area (n=9), including \u27feminist\u27. Participants then rated the head shots of the CEO\u27s on a 7 point Likert scale of communal (expected feminine) traits like attractiveness, warmth, compassion and cooperativeness, and on agentic (expected masculine) traits like ambition, leadership ability and intelligence. If college students hold negative stereotypes of feminists, feminist identified women are expected to be rated lower on levels of attractiveness, warmth, compassion and cooperativeness, but higher in leadership ability, ambition, and intelligence. Results demonstrated that participants did not hold negative stereotypes of feminists as they rated them similarly to environmentalists, progressives, and liberals. Results demonstrated that participants held negative stereotypes about conservatives and republicans

Cognitive styles of Indian, Metis, Inuit and non-Natives of northern Canada and Alaska and implications for education

The present study investigated the cognitive styles of Indian, Metis, Inuit and non-native adults and adolescents of northern Canada and Alaska. The study identified three relational and two analytical cognitive styles. The styles differed significantly from each other in relation to cultural background, language facility, level of post-secondary education, sex and age of the respondents. Cultural background was found to be the most significant discriminator of those under investigation. Procedure of the study involved the collection of verbalized responses to five open-ended questions concerning education from one hundred northern residents. A total of 528 minutes 32 seconds of taperecorded responses was available from twenty treaty and status Indians, twenty Metis, twenty Inuit and forty non-natives. Subjects included parents, university students, high school students, teacher trainees, teachers, education administrators, native politicians and general community members. The data were submitted to content analysis procedures with items coded according to the Data Analysis of Cognitive Style (DACS) Scale which had been adapted for use in the present study from the work of E. S. Schneidman (1966). Scale item frequencies for each respondent were tabulated and submitted for statistical analyses to the SPSS program discriminant analysis. This analysis identified significantly different functions which translated into patterns of thinking or cognitive styles. In addition this analysis identified the relative importance of functions as discriminators among groups and computed predictability scores which showed the percentage of respondents who were correctly classified according to cognitive styles. and demographic variables.Findings of this study must be considered in relation to the following limitations: the size and nature of the stratified random sample; the reliability of the coders; the use of the unvalidated DACS scale; the ability of the analytical procedures to correctly discriminate among the study groups. The study found that the groups which tended to think in relational styles were: Natives (Indian, Metis, Inuit), people with no university education or with less than one year at university; bilinguals (English and a native language); males; people under twenty years and over forty years of age. The terms Conflict-relational, Moral-relational and Inexactrelational were used to more precisely identify differing cognitive behaviors within the overall relational category. The groups which were found to exhibit analytical cognitive style behaviors included: the nonnative group; those respondents with two to four years of university education; and respondents between thirty and forty years of age. Subcategories within analytical styles were Conflict-analytical and Inexactanalytical.When the Indian, Metis and Inuit respondents were combined into a "native" cultural group they strongly identified with the Moral-relational cognitive style (people-oriented, subjective, holistic, concerned with morals and ethics). The non-native group showed a strong negative relationship to this style. However, when each cultural group was analyzed separately, it was found that the Indian and Inuit subjects were somewhat more analytical (objective, linear, field-independent) than the Metis but less so than the non-natives. On the analysis of four groups, the nonnatives were found to relate to both relational and analytical styles of thinking, indicating a wide range of differences within the group.It was concluded that significant differences existed in the cognitive styles preferred by respondents of different cultural, language, education, sex and age groups in this study. Cultural background was found to be the strongest discriminator in relation to cognitive style differences. It was further concluded that according to extrapolation of findings to the theoretical model it may be possible and desirable to modify curricula content and teaching techniques to achieve a closer match between teaching styles and cognitive and learning styles of. students of indigenous cultural backgrounds

Improved compactly computable objective measures for predicting the acceptiability of speech communications systems

Issued as Monthly status reports [1-7], and Final report, Project no. E-21-61

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Characterisation of voice quality of Parkinson’s disease using differential phonological posterior features

Change in voice quality (VQ) is one of the first precursors of Parkinson’s disease (PD). Specifically, impacted phonation and articulation causes the patient to have a breathy, husky-semiwhisper and hoarse voice. A goal of this paper is to characterise a VQ spectrum – the composition of non-modal phonations – of voice in PD. The paper relates non-modal healthy phonations: breathy, creaky, tense, falsetto and harsh, with disordered phonation in PD. First, statistics are learned to differentiate the modal and non-modal phonations. Statistics are computed using phonological posteriors, the probabilities of phonological features inferred from the speech signal using a deep learning approach. Second, statistics of disordered speech are learned from PD speech data comprising 50 patients and 50 healthy controls. Third, Euclidean distance is used to calculate similarity of non-modal and disordered statistics, and the inverse of the distances is used to obtain the composition of non-modal phonation in PD. Thus, pathological voice quality is characterised using healthy non-modal voice quality “base/eigenspace”. The obtained results are interpreted as the voice of an average patient with PD and can be characterised by the voice quality spectrum composed of 30% breathy voice, 23% creaky voice, 20% tense voice, 15% falsetto voice and 12% harsh voice. In addition, the proposed features were applied for prediction of the dysarthria level according to the Frenchay assessment score related to the larynx, and significant improvement is obtained for reading speech task. The proposed characterisation of VQ might also be applied to other kinds of pathological speech

Workshop on dimensional analysis for design, development, and research executives

The proceedings of a conference of research and development executives are presented. The purpose of the meeting was to develop an understanding of the conditions which are appropriate for the use of certain general management tools and those conditions which render these tools inappropriate. The verbatim statements of the participants are included to show the direction taken initially by the conference. Formal presentations of management techniques for research and development are developed