EMD-based noise estimation and tracking (ENET) with application to speech enhancement

Speech enhancement from measured speech signals is fundamental in a wide range of instruments. It relies on a noise estimate which can be obtained using techniques such as the minimum statistics (MS) approach. In this paper, a novel approach for Empirical Mode Decomposition (EMD) based noise estimation and tracking (EET) is presented with application to speech enhancement. Spectral analysis of nonstationary signals such as speech is performed effectively using EMD. The Improved Minima Controlled Recursive Averaging (IMCRA) that evolved from MS has been shown to be effective in non-stationary environments. EET is able to use EMD in a novel way to estimate the noise spectrum more accurately than IMCRA and enhance speech more effectively than conventional log-MMSE approaches. A comparative performance study is included that demonstrates that it achieves improved speech quality than a conventional log-MMSE filtering approach with better noise estimation, even during periods of strong speech activity

Local binary patterns for 1-D signal processing

Local Binary Patterns (LBP) have been used in 2-D image processing for applications such as texture segmentation and feature detection. In this paper a new 1-dimensional local binary pattern (LBP) signal processing method is presented. Speech systems such as hearing aids require fast and computationally inexpensive signal processing. The practical use of LBP based speech processing is demonstrated on two signal processing problems: - (i) signal segmentation and (ii) voice activity detection (VAD). Both applications use the underlying features extracted from the 1-D LBP. The proposed VAD algorithm demonstrates the simplicity of 1-D LBP processing with low computational complexity. It is also shown that distinct LBP features are obtained to identify the voiced and the unvoiced components of speech signal

Speech enhancement using adaptive empirical mode decomposition

Speech enhancement is performed in a wide and varied range of instruments and systems. In this paper, a novel approach to speech enhancement using adaptive empirical mode decomposition (SEAEMD) is presented. Spectral analysis of non-stationary signals can be performed by employing techniques such as the STFT and the Wavelet transform (WT), which use predefined basis functions. Empirical mode decomposition (EMD) performs very well in such environments. EMD decomposes a signal into a finite number of data-adaptive basis functions, called intrinsic mode functions (IMFs). The new SEAEMD system incorporates this multi-resolution approach with adaptive noise cancellation (ANC) for effective speech enhancement on an IMF level, in stationary and non-stationary noise environments. A comparative performance study is included that compares the competitive method of conventional ANC to the robust SEAEMD system. The results demonstrate that the new system achieves significantly improved speech quality with a lower level of residual noise