Design of Low Pass FIR Filter Using Artificial Neural Network
In signal processing, there are many instances in which an input signal to a system contains extra unnecessary content or additional noise which can degrade the quality of the desired portion. In such cases we may remove or filter out the useless samples. For example, in the case of the telephone system, there is no reason to transmit very high frequencies since most speech falls within the band of 400 to 3,400 Hz. Therefore, in this case, all frequencies above and below that band are filtered out. The frequency band between 400 and 3,400 Hz, which isnt filtered out, is known as the pass band, and the frequency band that is blocked out is known as the stop band. Finite Impulse Response, filters are one of the primary types of filters used in Digital Signal Processing. For the design of Low pass FIR filters complex calculations are required. Mathematically, by substituting the values of Pass band, transition width, pass band ripple, stop band attenuation, sampling frequency in any of the methods from window method, frequency sampling method or optimal method we can get the values of filter coefficients h(n).In this paper. Kaiser Window method has been chosen preferably because of the presence of ripple factor (p). Considering Low pass Filter design, the range of values for the parameters required are calculated.
Harpreet Kaur Balwinder Dhaliwal
BBSBEC.Fatehgarh Sahib GNDEC,Ldh
国际会议
重庆
英文
463-466
2011-01-21（万方平台首次上网日期，不代表论文的发表时间）