EMD-Based Filtering (EMDF) of Low-Frequency Noise for Speech Enhancement

摘要

An empirical mode decomposition-based filtering (EMDF) approach is presented as a postprocessing stage for speech enhancement. This method is particularly effective in low-frequency noise environments. Unlike previous EMD-based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low-frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimally modified log-spectral amplitude approach which uses a minimum statistics-based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise, and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results.