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NASA NTRSDate not supplied

A Zero-order Hold Approach for Fractional-delay Interpolation in Auralization

During the signal processing chain of an auralization simulating the propagation of a sound from a moving source to a stationary receiver, it is often necessary to interpolate between the samples of the source signal in order to arrive at uniformly-spaced samples at the receiver. In some cases, this interpolation is done in the receiver time frame – where the “input” samples of the source have become irregularly spaced due to time dilation effects. Canonical band-limited interpolation methods (i.e., sinc and sinc-derived approaches) cannot be applied in this case as they rely on having a uniformly-spaced input. The use of geometric interpolation methods that can handle irregularly-spaced input may not be grounded in signal processing principles and may produce unwanted artifacts and noise. This presentation outlines the possibility of embedding an irregularly-spaced zero-order hold signal within a highly over-sampled uniformly-spaced signal, and then processing down to the desired sampling rate through successive decimations. Initial distortion and noise characteristics of the approach are shown for some basic propagation geometries. The possible benefits of using such an approach in an auralization scheme with a time-varying Doppler shift are discussed including: the prevention of aliasing, processing time advantages, and the possibility for asynchronous processing.

Auralization↗
NASA NTRSDate not supplied

Exploring Flooded Fraction Prediction through Machine Learning Models Focusing on Medical Infrastructure in the Southeast U.S. Coastal Areas

Rising sea levels due to climate change increasingly threaten medical infrastructure through flooding. This study develops machine learning models to predict flood exposure for 11,508 medical facilities in the southeastern coastal regions of the United States by integrating datasets including meteorological, hydrological, topographic, and geological data, the Natural Risk Index, and historical flood records from NASA, HIFLD, and FEMA. Six regression models, namely Linear Regression, Support Vector Regression, Random Forest, k-Nearest Neighbors, XGBoost, and Artificial Neural Networks, are trained using 16 explanatory variables identified through literature review and correlation analysis. Data preprocessing employs the SMOGN for class imbalance and Winsorization for outliers. Model performance is evaluated using MAE, MSE, and RMSE, with Random Forest and XGBoost models achieving the highest performance (MSE of 2.58e-5 and 3.69e-5, respectively). This multifactorial approach allows the models to capture complex flood-influencing relationships, enhancing adaptability and performance across geographic regions. Future work focuses on expanding across the U.S. and developing a near real-time flood monitoring system.

Jihoon Chung↗