Noise Robust VAD Technique and its Performance Evaluation

Abstract

In this real world of audio and speech signals, the ability to detect the voice activity and remove the background voice is important. This project presents that to remove noise robust Voice Activity Detection (VAD) technique that combines voice source and vocal tract system information using a zero-frequency filtering (ZFF) approach. As mentioned earlier ZFF technique is employed to combine and to compute a composite signal that encapsulate essential parameters such as fundamental frequency and formats, enabling robust VAD in time domain.This methodology offers a significant advantage in terms of computational efficiency compared to other methods, making it an attractive choice for real-time applications. By applying dynamic thresholding after spectral entropy-based weighting, this approach exhibits resilience or minimum across a range of Signal-to-Noise Ratios (SNRs), further enhancing its ability.This project provides comprehensive utility for audio segmentation, which offers the capability to process individual audio files or entire directories. It also includes the extraction of a composite signal, which is a critical component in enhancing voice quality and reducing background noise. With this, the project is open to use build further and use it for the big industries purpose and for communication purpose. Keywords— Voice Activity Detection (VAD), Zero-Frequency Filtering (ZFF), Signal Noise Ratio (SNR).