Browsing by Subject "noise"

Now showing 1 - 5 of 5
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    Data for: Inconsistent sexual signaling degrades optimal mating decisions in animals
    (2020-03-09) Tanner, Jessie C; Bee, Mark A; jessie.c.tanner@gmail.com; Tanner, Jessie C; University of Minnesota Animal Communication Lab
    Data from a series of phonotaxis tests used to investigate the effects of within-individual variation (inconsistency) in male signals and ambient chorus noise on female mating decisions in Cope's gray treefrog. This dataset is among only a few generated to examine the effects of within-individual variation in signal production on animal communication. The data are now being released in support of a publication describing our findings.
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    Data for: Species recognition is constrained by chorus noise, but not inconsistency in signal production, in Cope's gray treefrog (Hyla chrysoscelis)
    (2020-06-18) Tanner, Jessie C; Bee, Mark A; jessie.c.tanner@gmail.com; Tanner, Jessie C; University of Minnesota Animal Communication Lab
    Optimal mate choice based on the assessment of communication signals can be constrained by multiple sources of noise. This dataset was created to examine the effects of two possible noise sources: ambient noise caused by the treefrog chorus and the inconsistency in signal production inherent to many animal communication systems. Our data were generated using two-choice phonotaxis tests of female Cope's gray treefrogs.
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    Local in space and time: Acoustic environmental policy in Minnesota and a fine-scale spatiotemporal representation of aircraft noise impact on residential life
    (2016-03) Bonsal, Dudley
    Communities near the Minneapolis-St. Paul International Airport (MSP) have been affected by significant levels of aircraft noise. The ways that residents are sensitive to the noise have been reflected in the conflicts over how best to regulate it, including how to adopt mapping techniques that accurately reflect the degree of their exposure and how to provide the appropriate amount of mitigation. In this dissertation, a mixed-method approach is adopted to examine how the acoustic environment, and aircraft noise in particular, are configured spatiotemporally in an urban, residential context. First, the legal designation of quietude as an acoustic natural resource in Minnesota is examined in regard to its implications for how aircraft noise exposure is regulated in the vicinity of MSP and how sound research can be reconceived on a broader scale. Next, a geospatial analysis of MSP aircraft departure patterns is adopted so that temporal variations are represented to better reflect the day-to-day noise exposure of local residents. Finally, a methodology is created for representing the cumulative impact of aircraft noise, based on changing departure patterns over time and the use of demographic data for the overall population, as well as sub-populations whose exposure varies based on the time spent at home. The project is guided throughout by three overarching concerns: the impact of environmental policy on the acoustic landscape, the urban acoustic environment from a residential perspective, and geographic representations of aircraft noise exposure at finer spatial and temporal scales.
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    Unsupervised methods to discover events from spatio-temporal data
    (2016-05) Chen, Xi
    Unsupervised event detection in spatio-temporal data aims to autonomously identify when and/or where events occurred with little or no human supervision. It is an active field of research with notable applications in social, Earth, and medical sciences. While event detection has enjoyed tremendous success in many domains, it is still a challenging problem due to the vastness of data points, presence of noise and missing values, the heterogeneous nature of spatio-temporal signals, and the large variety of event types. Unsupervised event detection is a broad and yet open research area. Instead of exploring every aspect in this area, this dissertation focuses on four novel algorithms that covers two types of important events in spatio-temporal data: change-points and moving regions. The first algorithm in this dissertation is the Persistence-Consistency (PC) framework. It is a general framework that can increase the robustness of change-point detection algorithms to noise and outliers. The major advantage of the PC framework is that it can work with most modeling-based change-point detection algorithms and improve their performance without modifying the selected change-point detection algorithm. We use two real-world applications, forest fire detection using a satellite dataset and activity segmentation from a mobile health dataset, to test the effectiveness of this framework. The second and third algorithms in this dissertation are proposed to detect a novel type of change point, which is named as contextual change points. While most existing change points more or less indicate that the time series is different from what it was before, a contextual change point typically suggests an event that causes the relationship of several time series changes. Each of these two algorithms introduces one type of contextual change point and also presents an algorithm to detect the corresponding type of change point. We demonstrate the unique capabilities of these approaches with two applications: event detection in stock market data and forest fire detection using remote sensing data. The final algorithm in this dissertation is a clustering method that discovers a particular type of moving regions (or dynamic spatio-temporal patterns) in noisy, incomplete, and heterogeneous data. This task faces two major challenges: First, the regions (or clusters) are dynamic and may change in size, shape, and statistical properties over time. Second, numerous spatio-temporal data are incomplete, noisy, heterogeneous, and highly variable (over space and time). Our proposed approach fully utilizes the spatial contiguity and temporal similarity in the spatio-temporal data and, hence, can address the above two challenges. We demonstrate the performance of the proposed method on a real-world application of monitoring in-land water bodies on a global scale.