Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

The emotional contents of the ‘space’ in spatial music

Human spatial perception is how we understand places. Beyond understanding what is where (William James’ formulation of the psychological approach to perception); there are holistic qualities to places. We perceive places as busy, crowded, exciting, threatening or peaceful, calm, comfortable and so on. Designers of places spend a great deal of time and effort on these qualities; scientists rarely do. In the scientific world-view physical qualities and our emotive responses to them are neatly divided in the objective-subjective dichotomy. In this context, music has traditionally constituted an item in a place. Over the last two decades, development of “spatial music” has been within the prevailing engineering paradigm, informed by psychophysical data; here, space is an abstract, Euclidean 3-dimensional ‘container’ for events. The emotional consequence of spatial arrangements is not the main focus in this approach. This paper argues that a paradigm shift is appropriate, from ‘music-in-a-place’ to ‘music-as-a-place’ requiring a fundamental philosophical realignment of ‘meaning’ away from subjective response to include consequences-in-the-environment. Hence the hegemony of the subjective-objective dichotomy is questioned. There are precedents for this, for example in the ecological approach to perception (Gibson). An ecological approach to music-as-environment intrinsically treats the emotional consequences of spatio-musical arrangement holistically. A simplified taxonomy of the attributes of artificial spatial sound in this context will be discussed

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Incorporating Structured Commonsense Knowledge in Story Completion

The ability to select an appropriate story ending is the first step towards perfect narrative comprehension. Story ending prediction requires not only the explicit clues within the context, but also the implicit knowledge (such as commonsense) to construct a reasonable and consistent story. However, most previous approaches do not explicitly use background commonsense knowledge. We present a neural story ending selection model that integrates three types of information: narrative sequence, sentiment evolution and commonsense knowledge. Experiments show that our model outperforms state-of-the-art approaches on a public dataset, ROCStory Cloze Task , and the performance gain from adding the additional commonsense knowledge is significant