Open Access Publications
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Open access publications by faculty, postdocs, and graduate students in the Department of Electrical and Computer Engineering
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Browsing Open Access Publications by Author "Badiey, Mohsen"
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Item Automatic detection and classification of bearded seal vocalizations in the northeastern Chukchi Sea using convolutional neural networks(The Journal of the Acoustical Society of America, 2022-01-19) Escobar-Amado, Christian D.; Badiey, Mohsen; Pecknold, SeanBearded seals vocalizations are often analyzed manually or by using automatic detections that are manually validated. In this work, an automatic detection and classification system (DCS) based on convolutional neural networks (CNNs) is proposed. Bearded seal sounds were year-round recorded by four spatially separated receivers on the Chukchi Continental Slope in Alaska in 2016–2017. The DCS is divided in two sections. First, regions of interest (ROI) containing possible bearded seal vocalizations are found by using the two-dimensional normalized cross correlation of the measured spectrogram and a representative template of two main calls of interest. Second, CNNs are used to validate and classify the ROIs among several possible classes. The CNNs are trained on 80% of the ROIs manually labeled from one of the four spatially separated recorders. When validating on the remaining 20%, the CNNs show an accuracy above 95.5%. To assess the generalization performance of the networks, the CNNs are tested on the remaining recorders, located at different positions, with a precision above 89.2% for the main class of the two types of calls. The proposed technique reduces the laborious task of manual inspection prone to inconstant bias and possible errors in detections.Item Seabed Characterization Experiment: Analysis of Broadband Data(IEEE Journal of Oceanic Engineering, 2021-12-17) Rajan, Subramaniam D.; Wan, Lin; Badiey, Mohsen; Wilson, Preston S.Analysis of data acquired during the seabed characterization experiment conducted in the New England mud patch area in March 2017 is presented in this article. A particular feature of these data is the presence of the Airy phase in some of the modes. The mode dispersion data along with the Airy phase information are extracted using the time warping method. The mode dispersion data obtained from the analysis of the acoustic data are then used to estimate the compressional wave speed and density profiles of the sediment layers using linear inversion methods. The validity of these results is investigated, and their validity is demonstrated. The results are consistent with the results obtained by the analysis of similar data in which a nonlinear inversion method was used to estimate the sediment properties. These results further show that meaningful results can be obtained using the linearized inversion procedure.