Abstract In this study, we present a deep learning-based speech signal-processing mobile application, known as CITISEN, which can perform three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC). For SE, CITISEN can effectively reduce noise components from speech signals and accordingly enhance their clarity and intelligibility....
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An Explainable Multi-Performances Predictor for Recommending Deployed Locations of New Bank Branches
Abstract Selecting the optimal location for a new bank branch is challenging but worth studying due to its importance to the success of the business. In recent years, the proliferation of multisource data in smart cities has promoted the development of the data-driven methods on this issue. Previous studies have...
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InQSS: a speech intelligibility assessment model using a multi-task learning network
Abstract Speech intelligibility assessment models are essential tools for researchers to evaluate and improve speech processing models. In this study, we propose InQSS, a speech intelligibility assessment model that uses both spectrogram and scattering coefficients as input features. In addition, InQSS uses a multi-task learning network in which quality scores...
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