By Saeed V. Vaseghi
Complicated electronic sign Processing and Noise aid, 3rd variation, presents a completely up to date and based presentation of the idea and functions of statistical sign processing and noise aid tools. Noise is the everlasting bane of communications engineers, who're continuously striving to discover new how one can enhance the signal-to-noise ratio in communications structures and this source can assist them with this activity. this can be a useful textual content for senior undergraduates, postgraduates and researchers within the fields of electronic sign processing, telecommunications and statistical facts research. it's going to additionally attract engineers in telecommunications and audio and sign processing industries.
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Additional resources for Advanced digital signal processing and noise reduction
1968) Information Theory and Reliable Communication. Wiley, New York. HAYKIN S. (1991) Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs, NJ. HAYKIN S. (1985) Array Signal Processing. Prentice-Hall, Englewood Cliffs, NJ. KAILATH T. (1980) Linear Systems. Prentice Hall, Englewood Cliffs, NJ. E. (1960) A New Approach to Linear Filtering and Prediction Problems. Trans. of the ASME, Series D, Journal of Basic Engineering, 82, pp. 35–45. M. (1993) Fundamentals of Statistical Signal Processing, Estimation Theory.
However, it must be noted that the relatively lower-energy high-frequency part of audio signals plays an important part in conveying sensation and quality. 9(a) and (b) show examples of the spectra of car noise recorded from a BMW and a Volvo respectively. The noise in a car is nonstationary, and varied, and may include the following sources: (a) quasi-periodic noise from the car engine and the revolving mechanical parts of the car; (b)noise from the surface contact of wheels and the road surface; (c) noise from the air flow into the car through the air ducts, windows, sunroof, etc; (d) noise from passing/overtaking vehicles.
The simplest method for noise modelling, often used in current practice, is to estimate the noise statistics from the signal-inactive periods. In optimal Bayesian signal processing methods, a set of probability models are trained for the signal and the noise processes. The models are then used for the decoding of the underlying states of the signal and noise, and for noisy signal recognition and enhancement. 1 Additive White Gaussian Noise Model (AWGN) In communication theory, it is often assumed that the noise is a stationary additive white Gaussian (AWGN) process.
Advanced digital signal processing and noise reduction by Saeed V. Vaseghi