Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada

Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada

Two-dimensional FIR compaction filter design

The design of signal-adapted multirate filter banks has been an area of research interest. The authors present the design of a 2-D finite impulse response (FIR) compaction filter followed by a 2-D FIR filter bank that packs the maximum energy of the input process into a few subbands. The energy compaction property of the 2-D compaction filter is extremely good for higher filter orders and converges to the ideal optimal solution as the order tends to infinity. The design procedure is very straightforward and involves a 2-D spectral factorisation

Detection performance of an adaptive MTD with WVD as a Doppler filter bank

The Wigner–Ville Distribution (WVD) has been gaining importance in the recent years for the analysis of time varying signals (B. Boashash, in: Simon Haykin (Ed.), Advances in Spectrum Analysis and Array Processing, Prentice-Hall, Englewood Cliffs, NJ, 1991). In this paper, an adaptive moving target detector (AMTD) based on WVD as a Doppler filter bank has been introduced. The proposed AMTD processor has been simulated in the presence of Gaussian and non-Gaussian clutter environments. The preliminary investigations reveal that the proposed processor performs better in terms of probability of detection than the existing moving target detector (MTD) processors

Fixed-point error analysis of two DCT algorithms

A fixed-point error analysis of two fast DCT algorithms proposed by Hou (1987) and Makhoul (1980) is presented. Expressions for error variances are derived and the results are compared with the simulation results. It is found that the simulation results and analysis results agree quite closely. This demonstrates the validity of the analysis. In addition, the two algorithms are compared in terms of their advantages and disadvantages

Robust features for environmental sound classification

International audienceIn this paper we describe algorithms to classify environmental sounds with the aim of providing contextual information to devices such as hearing aids for optimum performance. We use signal sub-band energy to construct signal-dependent dictionary and matching pursuit algorithms to obtain a sparse representation of a signal. The coefficients of the sparse vector are used as weights to compute weighted features. These features, along with mel frequency cepstral coefficients (MFCC) are used as feature vectors for classification. Experimental results show that the proposed method gives a maximum accuracy of 95.6 % while classifying 14 categories of environmental sound using a gaussian mixture model (GMM)