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Voice Activity Detection (VAD) in Noisy Environments
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In the realm of digital audio processing, Voice Activity Detection (VAD) plays a pivotal role in distinguishing speech from non-speech elements, a task that becomes increasingly complex in noisy environments. This paper details the development and implementation of a VAD system, specifically engineered to maintain high accuracy in the presence of various ambient noises. We introduce a novel algorithm enhanced with a specially designed filtering technique, effectively isolating speech even amidst diverse background sounds. Our comprehensive testing and validation demonstrate the system's robustness, highlighting its capability to discern speech from noise with remarkable precision. The exploration delves into: (1) the core principles underpinning VAD and its crucial role in modern audio processing; (2) the methodologies we employed to filter ambient noise; and (3) a presentation of evidence affirming our system's superior performance in noisy conditions.
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Cited by 1 Pith paper
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SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization
A lightweight VAD model with a learnable sinc filterbank and a squared-margin ranking loss reports higher AUROC and F2 scores on AVA-Speech and ACAM using only 8.0k parameters.
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