Automatic systems for estimating operator fatigue have application in safety-critical environments. A system which could estimate level of fatigue from speech would have application in domains where operators engage in regular verbal communication as part of their duties. Previous studies on the prediction of fatigue from speech have been limited because of their reliance on subjective ratings and because they lack comparison to other methods for assessing fatigue. In this paper, we present an analysis of voice recordings and psychophysiological test scores collected from seven aerospace personnel during a training task in which they remained awake for 60 h. We show that voice features and test scores are affected by both the total time spent awake and the time position within each subject’s circadian cycle. However, we show that time spent awake and time-of-day information are poor predictors of the test results, while voice features can give good predictions of the psychophysiological test scores and sleep latency. Mean absolute errors of prediction are possible within about 17.5% for sleep latency and 5–12% for test scores. We discuss the implications for the use of voice as a means to monitor the effects of fatigue on cognitive performance in practical applications.
Sound zone systems aim to produce regions within a room where listeners may consume separate audio programs with minimal acoustical interference. Often, there is a trade-off between the acoustic contrast achieved between the zones and the fidelity of the reproduced audio program (the target quality). An open question is whether reducing contrast (i.e., allowing greater interference) can improve target quality. The planarity control sound zoning method can be used to improve spatial reproduction, though at the expense of decreased contrast. Hence, this can be used to investigate the relationship between target quality (which is affected by the spatial presentation) and distraction (which is related to the perceived effect of interference). An experiment was conducted investigating target quality and distraction and examining their relationship with overall quality within sound zones. Sound zones were reproduced using acoustic contrast control, planarity control, and pressure matching applied to a circular loudspeaker array. Overall quality was related to target quality and distraction, each having a similar magnitude of effect; however, the result was dependent upon program combination. The highest mean overall quality was a compromise between distraction and target quality, with energy arriving from up to 15 degrees either side of the target direction. INTRODUCTIONSound zone systems aim to control sound fields in such a way that multiple listeners can enjoy different audio programs within the same room. Conceptually, the overall quality of the sound zone listening experience could be considered to be the result of some combination of the effect of the presence of an interferer program and the effect of any artifacts or degradations to the target program (i.e., target quality) caused by the sound zone processing. A similar conceptual framework was utilized in [1]. While the relationship between the effect of the interferer and the effect of target quality degradations is unclear, a considerable body of research exists on these topics individually.Fields of research investigating the effect of auditory interferers include: the perception of environmental noise [2,3], the perception of multiple talkers [4], source separation [5], and combinations of these [6]. These studies generally do not consider common domestic interferers, such as music or sound effects in films; and where they do, they either do not isolate the interferer effect or they include artifacts and degradations that may be specific to source separation algorithms.In [7] a series of elicitation experiments were conducted to investigate terms describing auditory interference scenarios using ecologically valid programs (i.e., those that are commonly consumed in domestic environments). The results, and those of [8], showed that using the term "distraction" produced good agreement between listeners, and that listener ratings made using this term were a good measure of the perceived effect of the interferer. It seems likely, therefore, that there would be...
Understanding the complex biology of the tumor microenvironment (TME) is necessary to understand the mechanisms of action of immuno-oncology therapies and to match the right therapies to the right patients. Multiplex immunofluorescence (mIF) is a useful technology that has tremendous potential to further our understanding of cancer patho-biology; however, tools that fully leverage the high dimensionality of this data are still in their infancy. We describe here a novel deep learning pipeline aimed to allow Graph-based Inspection of Tissues via Embeddings, GraphITE. GraphITE transforms mIF data into a graph representation, where unsupervised learning algorithms can be utilised to generate embeddings representing cellular `neighbourhoods'. The embeddings can be downprojected and explored for clustering analysis, and patterns can be mapped back to the image as well as interrogated for phenotypical, morphological, or structural distinctiveness. GraphITE supports the extraction of information not only on the phenotypes of individual cells or the relationships between specific cell types, but is able to characterize cell neighborhoods to look for more complex interactions, thereby allowing pathologists and data scientists to explore mIF data sets, uncovering patterns that are otherwise obscured by the high-dimensionality of the data. In this work, we showcase the current setup of the system, going from raw input data all the way to a user friendly exploration tool. Using this tool, we show how the data can be navigated in a way previously not possible.
Abstract. Automatic systems for estimating operator fatigue have application in safety-critical environments. We develop and evaluate a system to detect fatigue from speech recordings collected from speakers kept awake over a 60-hour period. A binary classification system (fatigued/not-fatigued) based on time spent awake showed good discrimination, with 80% unweighted accuracy using raw features, and 90% with speaker-normalised features. We describe the data collection, feature analysis, machine learning and cross-validation used in the study. Results are promising for real-world applications in domains such as aerospace, transportation and mining where operators are in regular verbal communication as part of their normal working activities.
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