A Novel Turbo ICI & ICI DSP Cancellation Technique for FBMC-OQAM through a Doubly Selective Channel
Abstract
Voice Activity Detection (VAD) plays a crucial role in various speech processing appli- cations, such as speech recognition, telecommunication, and speech enhancement. Traditional VAD methods, however, struggle to maintain high accuracy in noisy environments, particularly when the Signalto-Noise Ratio (SNR) is low. This paper explores the use of wavelet transform-based techniques to improve VAD performance in real-world noisy environments. Two wavelet transform-based VAD algorithms, Algorithm-1 and Algorithm2, are introduced and evaluated across four different noise types (airport, babble, restaurant, and station) and at four SNR levels (0 dB, 5 dB, 10 dB, and 15 dB). The performance of the algorithms is measured using two objective metrics: Frame Error Rate (FER) and F1 score. The results show that Algorithm-2 outperforms Algorithm-1 in all tested conditions, offering lower FER and higher F1 scores, demonstrating its robustness in noise-robust VAD. These findings suggest that wavelet transform-based methods provide a promising solution for improving VAD performance, particularly in challenging acoustic environments with varying noise conditions.
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