Estimation and Tuning of FIR Lowpass Digital Filter Parameters

Abstract

Abstract-Finite impulse response (FIR) digital filters are known to have many distinguishable features such as stability, linear phase characteristic at all frequencies and digital implementation as non-recursive structures. FIR filter design can be considered as an optimization problem. In this paper an estimation method of FIR filter parameters is proposed. The method relies on establishing a relationship between the signal input parameters and the filter parameters. An FIR lowpass filter was implemented and tested using various parameter values. The results showed efficient performance characteristics of FIR lowpass filters.Keywords-FIR lowpass filter, transition bandwidth, sampling frequency, window length, filter order, and stopband attenuation. I. INTRODUCTIONDesigning digital filters involves the determination of a set of filter coefficients to meet a set of design specifications. Digital filters can be classified in two categories: finite impulse response (FIR) filters and infinite impulse response (IIR) filters. By varying the weight of the coefficients and number of filter taps, virtually any frequency response characteristics can be realized with an FIR filter. FIR filter is an attractive choice because of the ease in design, linear phase shift property and stabilityDesigning an FIR digital filter require specifying passband, stopband, and transition band. In passband, frequencies are needed to be passed unattenuated. In stopband, frequencies needs to be passed attenuated. Transitionband contains frequencies which are lying between the passband and stopband. Therefore, the entire frequency range is split into one or even more passbands, stopbands, and transition bands. In practical, the magnitude is not necessary to be constant in the passband of a filter. A small amount of ripple is usually allowed in the passband. Similarly, the filter response does not to be zero in the stopband. A small, nonzero value is also tolerable in the stopband as shown in Fig 1. The transition band of the filter as shown in Fig.1 is between the passband and the stopband. The frequency ω p denotes the edge of the passband, and the band-edge frequency ωs defines the edge of the stopband. So, the difference of ω s and ω p is the width of the transition band, i.e. ω t = ω s -ω p .The ripple in the passband of the filter is denoted as δp, and the magnitude of the filter varies from 1-δp to 1+ δp. δs are the ripple in the stopband.Usually we use a Logarithmic scale to show the frequency response, hence, the ripple in the passband is 20log10δp dB, and the ripple in the stopband is 20log10δs dB. [[3], [4]] Different methods are used to design a lowpass filter such as: window method, frequency sampling method, and optimization method.In window method, a truncated ideal lowpass filter with a certain bandwidth is generated, and then we use a chosen window to get certain stopband attenuation. The filter order L can be adjusted to meet a specified roll-off rate in the transition band. Any finite-length (order) of the ideal lowpass impu...

Analysis of Digital Signal Features Extraction Based on LBP Operator

“…[32], [33]. Various authors proposed LBP and CSLBP (center symmetric LBP) methods [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], these methods were directed for image manipulation, and sometimes they were used to extract features for both gray and digital images [20] [21], Figure 3 Based on LBP [20][21][22][23][24][25][26][27][28][29][30] we introduce a modified LBP (MLBP) method to create a voiceprint for a digital voice signal, these voiceprint can be used in a recognition system to find a classifier that can identify the voice signal, MLBP can be implemented applying the following steps:…”

Enhancing Color Image Clustering using K-Means Method

“…For the process of image identification we have to seek a method of image features creation, those features can be used later to identify the image. Many methods were proposed for image features extraction [16][17][18][19][20][21][22], but here in this paper we will take the concentration on Kmeans clustering method [23], [24]. The image features extraction method must characterize by the following [25], [26], [27]:  Features size must be significantly small (minimizing the number of elements in the features array) [17], [18].…”

Untitled

“…(1) For 4 order FIR filter equation 1 can be represented using equation 2 [20]: Using LPC matlab function we can determine FIR filter coefficients for any filter order, these coefficients can be used later by a filter function with z-transform shown in equation 3 to generate the estimated voice signal:…”

“…Digital signals [1], [2], [3] such as color digital images [4], [5], [6], digital voices [7], [8] are the most papular digita l data types used by various applications [9], [10] such as security systems [19], identification system [20], [21] and human recognition systems [11], [12]. Information in the voice signal is embedded in both its time and frequency domains, within these domains; information relevant to profiling may be present in the patterns exhibited by specific characteristics of the voice signal.…”