Voice activity detection in transient noise environment using Laplacian pyramid algorithm
Nurit Spingarn, Saman Mousazadeh, Israel Cohen · 2014
Voice activity detection (VAD) has attracted significant research efforts in the last two decades. Despite much progress in designing voice activity detectors, voice activity detection in presence of transient noise and low SNR is a challenging problem. In this paper, we propose a new VAD algorithm based on supervised learning. Our method employs Laplacian pyramid algorithm as a tool for function extension. We estimate the likelihood ratio function of unlabeled data, by extending the likelihood ratios obtained from the labeled data. Simulation results demonstrate the advantages of the proposed method in transient noise environments over conventional statistical methods.