By Saeed V. Vaseghi(auth.)
Electronic sign processing performs a imperative position within the improvement of contemporary communique and data processing platforms. the speculation and alertness of sign processing is anxious with the id, modelling and utilisation of styles and constructions in a sign technique. The statement signs are usually distorted, incomplete and noisy and as a result noise relief, the elimination of channel distortion, and substitute of misplaced samples are very important elements of a sign processing approach.
The fourth version of Advanced electronic sign Processing and Noise Reduction updates and extends the chapters within the past variation and comprises new chapters on MIMO structures, Correlation and Eigen research and self sufficient part research. the wide variety of themes coated during this ebook contain Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and elimination of impulsive and temporary noise, interpolation of lacking information segments, speech enhancement and noise/interference in cellular communique environments. This ebook offers a coherent and dependent presentation of the idea and functions of statistical sign processing and noise relief tools.
new chapters on MIMO structures, correlation and Eigen research and self reliant part research
accomplished insurance of complicated electronic sign processing and noise aid tools for communique and knowledge processing platforms
Examples and purposes in sign and data extraction from noisy facts
- Comprehensive yet available assurance of sign processing idea together with likelihood types, Bayesian inference, hidden Markov versions, adaptive filters and Linear prediction types
Advanced electronic sign Processing and Noise Reduction is a useful textual content for postgraduates, senior undergraduates and researchers within the fields of electronic sign processing, telecommunications and statistical information research. it is going to even be of curiosity to expert engineers in telecommunications and audio and sign processing industries and community planners and implementers in cellular and instant communique communities.Content:
Chapter 1 advent (pages 1–33):
Chapter 2 Noise and Distortion (pages 35–50):
Chapter three details concept and chance types (pages 51–105):
Chapter four Bayesian Inference (pages 107–146):
Chapter five Hidden Markov versions (pages 147–172):
Chapter 6 Least sq. errors Wiener?Kolmogorov Filters (pages 173–191):
Chapter 7 Adaptive Filters: Kalman, RLS, LMS (pages 193–225):
Chapter eight Linear Prediction types (pages 227–255):
Chapter nine Eigenvalue research and critical part research (pages 257–270):
Chapter 10 strength Spectrum research (pages 271–294):
Chapter eleven Interpolation – alternative of misplaced Samples (pages 295–320):
Chapter 12 sign Enhancement through Spectral Amplitude Estimation (pages 321–339):
Chapter thirteen Impulsive Noise: Modelling, Detection and elimination (pages 341–358):
Chapter 14 brief Noise Pulses (pages 359–369):
Chapter 15 Echo Cancellation (pages 371–390):
Chapter sixteen Channel Equalisation and Blind Deconvolution (pages 391–421):
Chapter 17 Speech Enhancement: Noise relief, Bandwidth Extension and Packet alternative (pages 423–466):
Chapter 18 Multiple?Input Multiple?Output platforms, self sustaining part research (pages 467–490):
Chapter 19 sign Processing in cellular conversation (pages 491–508):
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Additional info for Advanced Digital Signal Processing and Noise Reduction, Fourth Edition
When the channel response is unknown the process of signal recovery is called blind equalisation. Blind equalisation has a wide range of applications, for example in digital telecommunications for removal of inter-symbol interference due to non-ideal channel and multi-path propagation, in speech recognition for removal of the effects of the microphones and the communication channels, in correction of distorted images, analysis of seismic data, de-reverberation of acoustic gramophone recordings etc.
X(f) High frequency spectrum aliasing into low frequency parts Base-band spectrum Low frequency spectrum aliasing into high frequency parts …. …. …. -2Fs -Fs Fs 2Fs …. 25 Aliasing distortion results from the overlap of spectral images (dashed curves) with the baseband spectrum. Note high frequency aliases itself as low frequency and vice versa. In this example the signal is sampled at half the required rate. 26 Illustration of aliasing. Top panel: the sum of two sinewaves, the assumed frequencies of the sinewaves are 6200 Hz and 12 400 Hz, the sampling frequency is 40 000 Hz.
The effect of the vibrations of the glottal cords and the resonance of the vocal tract is to shape the frequency spectrum of speech and introduce a measure of correlation and predictability on the random variations of the air from the lungs. 13 illustrates a source-ﬁlter model for speech production. 13 Vocal tract model H(z) Linear predictive model of speech. Speech 16 Introduction and emits a random excitation signal which is ﬁltered, ﬁrst by a pitch ﬁlter model of the glottal cords and then by a model of the vocal tract.