## DSP for MATLAB and LabVIEW: LMS adaptive filteringThis book is Volume IV of the series DSP for MATLABâ„˘ and LabVIEWâ„˘. Volume IV is an introductory treatment of LMS Adaptive Filtering and applications, and covers cost functions, performance surfaces, coefficient perturbation to estimate the gradient, the LMS algorithm, response of the LMS algorithm to narrow-band signals, and various topologies such as ANC (Active Noise Cancelling) or system modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with single- and dual-H topologies), and Inverse Filtering/Deconvolution. The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts (of which there are more than 200) described in the text and supplied in code form (available via the internet at www.morganclaypool.com/page/isen) will run on both MATLABâ„˘ and LabVIEWâ„˘. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEWâ„˘ Virtual Instruments (VIs) that can be run to illustrate various signal processing concepts graphically on the user's computer screen. Volume I consists of four chapters that collectively set forth a brief overview of the field of digital signal processing, useful signals and concepts (including convolution, recursion, difference equations, LTI systems, etc), conversion from the continuous to discrete domain and back (i.e., analog-to-digital and digital-to-analog conversion), aliasing, the Nyquist rate, normalized frequency, sample rate conversion and Mu-law compression, and signal processing principles including correlation, the correlation sequence, the Real DFT, correlation by convolution, matched filtering, simple FIR filters, and simple IIR filters. Chapter 4 of Volume I, in particular, provides an intuitive or "first principle" understanding of how digital filtering and frequency transforms work. Volume II provides detailed coverage of discrete frequency transforms, including a brief overview of common frequency transforms, both discrete and continuous, followed by detailed treatments of the Discrete Time Fourier Transform (DTFT), the z-Transform (including definition and properties, the inverse z-transform, frequency response via z-transform, and alternate filter realization topologies including Direct Form, Direct Form Transposed, Cascade Form, Parallel Form, and Lattice Form), and the Discrete Fourier Transform (DFT) (including Discrete Fourier Series, the DFT-IDFT pair, DFT of common signals, bin width, sampling duration, and sample rate, the FFT, the Goertzel Algorithm, Linear, Periodic, and Circular convolution, DFT Leakage, and computation of the Inverse DFT). Volume III covers digital filter design, including the specific topics of FIR design via windowed-ideal-lowpass filter, FIR highpass, bandpass, and bandstop filter design from windowed-ideal lowpass filters, FIR design using the transition-band-optimized Frequency Sampling technique (implemented by Inverse-DFT or Cosine/Sine Summation Formulas), design of equiripple FIRs of all standard types including Hilbert Transformers and Differentiators via the Remez Exchange Algorithm, design of Butterworth, Chebyshev (Types I and II), and Elliptic analog prototype lowpass filters, conversion of analog lowpass prototype filters to highpass, bandpass, and bandstop filters, and conversion of analog filters to digital filters using the Impulse Invariance and Bilinear Transform techniques. Certain filter topologies specific to FIRs are also discussed, as are two simple FIR types, the Comb and Moving Average filters. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Introduction To LMS Adaptive Filtering | 1 |

12 SOFTWARE FOR USE WITH THIS BOOK | 2 |

14 PERFORMANCE SURFACE | 5 |

16 METHOD OF STEEPEST DESCENT | 7 |

17 TWO VARIABLE PERFORMANCE SURFACE | 14 |

18 AN IMPROVED GRADIENT SEARCH METHOD | 19 |

19 LMS USED IN AN FIR | 20 |

192 DERIVATION | 22 |

25 PERIODIC COMPONENT ELIMINATION OR ENHANCEMENT | 61 |

26 INTERFERENCE CANCELLATION | 65 |

27 EQUALIZATIONDECONVOLUTION | 69 |

28 DECONVOLUTION OF A REVERBERATIVE SIGNAL | 74 |

282 SPECTRAL EFFECT OF REVERBERATION | 76 |

283 ESTIMATING DELAY | 78 |

284 ESTIMATING DECAYRATE | 79 |

285 DECONVOLUTION | 80 |

193 LIMITATION ON Mu | 24 |

194 NLMS ALGORITHM | 25 |

110 CONTRASTTRUE MSE | 34 |

111 LMS ADAPTIVE FIR SUMMARY | 37 |

112 REFERENCES | 39 |

Applied Adaptive Filtering | 49 |

22 ACTIVE NOISE CANCELLATION | 50 |

23 SYSTEM MODELING | 51 |

24 ECHO CANCELLATION | 54 |

241 SINGLEH | 57 |

242 DUALH | 58 |

243 SPARSE COMPUTATION | 60 |

29 REFERENCES | 81 |

210 EXERCISES | 82 |

Software for Use with this Book | 99 |

A2 DOWNLOADING THE SOFTWARE | 100 |

A5 MULTILINE MCODE EXAMPLES | 101 |

A6 HOW TO SUCCESSFULLY COPYANDPASTE MCODE | 102 |

A7 LEARNING TO USE MCODE | 103 |

VectorMatrix Operations in MCode | 105 |

B22 OUTER PRODUCT | 106 |

B4 MATRIX INVERSE AND PSEUDOINVERSE | 107 |

Biography | 109 |

### Other editions - View all

### Common terms and phrases

2-tap FIR Active Noise Cancellation amplitude audio ﬁle autocorrelation autocorrelation sequence Channel Impulse Response chapter coefﬁcient estimates coefﬁcient track coefﬁcient update computed convergence cost function cTest DecayRate deconvolution deﬁned desired signal digital signal processing Dual-H mode echo cancellation End signal End speech ERLE error signal example ﬁgure Filter ﬁlter coefﬁcients ﬁlter output ﬁrst Freq Frequency response global impulse response input arguments input signal Interference Cancellation inverse LabVIEW lenLMS LMS adaptive ﬁlter LMS adaptive FIR LMS algorithm LVxLM SDeCorrHeterodyneCdrwatsonSR8K.wav magnitude MATLAB matrix microphone modeled Mute NLMS NoTaps number of samples number of taps parameters partial derivative path PeakSep performance surface Plant Coefﬁcients plot reverberated reverberative Sample Output/Error sample rate scalar error shown in Fig signal processing sine wave sinusoid slope speciﬁed StepSize subplot tap weights Test calls test signal tSig update term weight Volume weighting function white noise Write the m-code y-intercept z-transform zero