Objective comparison of speech enhancement algorithms under real world conditions

Over the past decades the problem of one channel, speech enhancement has been addressed by a great deal of researchers. In this work selected methods belonging to a variety of categories are applied to denoise speech signals corrupted by non-stationary urban noise. The performance of spectral subtraction, signal subspace, model-based and Kalman filtering approaches is evaluated. Several objective measures which are designed to predict human listening tests are employed in order to reach accurate conclusions. Two series of experiments were carried out while multiband spectral subtraction along with a short-time spectral amplitude (STSA) estimator based on the minimization of the mean square error (MSE) of the log-spectra are shown to outperform the rest of the algorithms.