Signal-dependent Representation Research Articles

Empirical Mode Decomposition in Discrete Time Signals Denoising

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Transform representation of the spectra of acoustic speech segments with applications. II. Speech analysis, synthesis, and coding

For Part I see ibid., vol.1, no.2, p.180-95 (1993). In Part I of this paper, the authors introduced an approach to the representation of the speech spectral envelope which makes use of the Karhunen-Loeve (KL) transformation of acoustic subword segments. This signal-dependent representation captures, with a few KL vectors and transform coefficients, the perceptually and phonetically important structure of the spectral envelope. Here the authors apply this representation to the analysis, synthesis, and coding of speech. They propose simple quantization and coding strategies for the KL representation vectors as well as for the resulting transform coefficients. The resulting technique is a variable rate encoding scheme which achieves good speech quality at an average rate of 3.5 kb/s. >