Minima controlled noise estimation for KLT-based speech enhancement

This paper addresses the problem of noise estimation for the Karhunen-Loeve transform (KLT) based speech enhancement. The eigenvalues and eigenvectors of the noise covariance matrix are tracked using recursive averaging algorithm. This process is controlled by the noise power minima obtained from the noisy signal even during the speech activity periods. The proposed approach is especially recommended for a class of signal subspace methods where a whitening transformation is required. Experiments show that the noise tracking algorithm offers similar performance as the method based on idealized voice activity detector (VAD).