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

• Published in: IEEE transactions on audio, speech, and language processing Vol. 20; no. 4; pp. 1158 - 1166
• Main Authors: Chatlani, N., Soraghan, J. J.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.05.2012; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; denoising; Detection, estimation, filtering, equalization, prediction; Empirical mode decomposition (EMD); Estimation; Exact sciences and technology; Information, signal and communications theory; Low-frequency noise; noise estimation; Noise measurement; Noise reduction; Signal and communications theory; Signal processing; Signal, noise; Speech; Speech enhancement; Speech processing; Telecommunications and information theory; Performance evaluation; Second order; Filtering; Acoustic signal; Empirical mode decomposition (EMD); Noise reduction; Decomposition method; Empirical method; Order statistic; 1/f noise; Background noise; Noise measurement; Adaptive method; Military application; Statistical method; denoising; Gaussian noise; Vocal signal; Residual impurity; Speech enhancement; Signal processing; Speech processing; noise estimation; Comparative study
• ISSN: 1558-7916; 1558-7924
• DOI: 10.1109/TASL.2011.2172428

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.