By Gillian M. Davis
Noise and distortion that degrade the standard of speech signs can come from any variety of assets. The expertise and methods for facing noise are virtually as quite a few, however it is simply lately, with the advance of cheap electronic sign processing undefined, that the implementation of the know-how has turn into practical.Noise relief in Speech functions presents a complete advent to trendy thoughts for elimination or lowering heritage noise from more than a few speech-related functions. Self-contained, it begins with a tutorial-style bankruptcy of historical past fabric, then specializes in procedure facets, electronic algorithms, and implementation. the ultimate part explores a number of functions and demonstrates to power clients of the know-how the implications attainable with the noise relief recommendations offered. The ebook bargains chapters contributed by means of foreign specialists, a realistic, structures procedure, and diverse references. For electric, acoustics, sign processing, communications, and bioengineers, Noise relief in Speech functions is a priceless source that exhibits you the way to make a decision no matter if noise relief will resolve difficulties on your personal platforms and the way to make the easiest use of the applied sciences on hand.
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Additional info for Noise Reduction in Speech Applications (Electrical Engineering & Applied Signal Processing Series)
17 are available. , References 1, 3, and 8). 41). vp Thursday, April 25, 2002 9:27:37 AM Color profile: Disabled Composite Default screen 0949_frame_C01 Page 27 Tuesday, March 5, 2002 10:59 AM tially noncausal result, only the causal part is realizable and is retained as the solution. , g[n] = d[n]. 43) Intuitively, it can be seen that for frequencies where the noise PSD, Pvv(ejW), takes on large values, the Wiener filter will attain a small gain and therefore attenuate the noise. 43). 43) addresses a very basic noise suppression problem, and for the previous filter design example in the previous section, “Filter Design,” to suppress mains interference the Wiener filter would similarly provide a bandstop filter at 50 Hz.
By thresholding and inverse transformation, noise reduction can be achieved. respect to the achievable parameterization and hence sparseness of the data in the transform domain. Noise Reduction Based on Signal Transforms The parameterization property of discrete transforms highlighted in the previous section can be exploited for noise reduction. If a signal is corrupted by random noise in the time domain, the idea is to find a suitable discrete transform that parameterizes the signal component of interest.
Filter Design Noise distortion of a signal of interest may be restricted to a certain frequency band, such as a jammer signal or mains hum as sources of interference. Therefore, the design of a filter with a specified frequency response H(ejW) to suppress these noise components is desirable. 13 Four different basic filter types as defined by their magnitude response: (a) low-pass, (b) highpass, (c) bandpass, and (d) bandstop filter. 13. A bandstop filter with a notch in its frequency response at 50 Hz can, for example, be used to attenuate mains interference on a signal.