Design and Implementation of Butterworth, Chebyshev-I and Elliptic Filter for Speech Signal Analysis

Podder, Prajoy; Hasan, Md. Mehedi; Islam, Md. Rafiqul; Sayeed, Mursalin

doi:10.5120/17195-7390

Cited by 34 publications

(17 citation statements)

References 9 publications

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“…All filters were designed for different applications. Butterworth filters were suggested for applications where maximum pass band flatness is required ("flat maximally magnitude" filters) [23], while Chebyshev filters are for rapid transition from passband to stopband. On the one hand, the CMA filter is the easiest digital filter to use, while on the other it is optimal for reducing white random noise keeping a sharp transition from passband to stopband [24], while the median filter is for Laplace noise.…”

Section: Discussionmentioning

confidence: 99%

Wave Signatures in Total Electron Content Variations: Filtering Problems

2020

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Over recent years, global navigation satellite systems (GNSSs) have been increasingly used to study near-Earth space. The basis for such studies is the total electron content (TEC) data. Standard procedures for detecting TEC wave signatures include variation selection and detrending. Our experience showed that the inaccurate procedure causes artifacts in datasets which might affect data interpretation, particularly in automated processing. We analyzed the features of various detrending and variation selection methods. We split the problem of the GNSS data filtering into two subproblems: detrending and variation selection. We examined centered moving average, centered moving median, 6th-order polynomial, Hodrick–Prescott filter, L1 filter, cubic smoothing spline, double-applied moving average for the GNSS-TEC detrending problem, and centered moving average, centered moving median, Butterworth filter, type I Chebyshev filter for the GNSS-TEC variation selection problem in this paper. We carried out the analysis based on both model and experimental data. Modeling was based on simple analytical models as well as the International Reference Ionosphere. Analysis of TEC variations of 2–10 min, 10–20 min, and 20–60 min under insufficient detrending conditions showed that the higher errors appear for the longer periods (20–60 min). For the detrending problem, the smoothing cubic spline provided the best results among the algorithms discussed in this paper. The spline detrending featured the minimal value of the mean bias error (MBE) and the root-mean-square error (RMSE), as well as high correlation coefficient. The 6th-order polynomial also produced good results. Spline detrending does not introduce a RMSE more than 0.25 TECU and MBE > 0.35 TECU for IRI trends, while, for the 6th-order polynomial, these errors can exceed 1 TECU in some cases. However, in 95% of observations the RMSE and MBE do not exceed 0.05 TECU. For the variation selection, the centered moving average filter showed the best performance among the algorithms discussed in this paper.

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Section: Discussionmentioning

confidence: 99%

Wave Signatures in Total Electron Content Variations: Filtering Problems

2020

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“…LP is in the range of [1][2][3][4][5] Hz, VT is in [2][3][4][5][6][7][8][9][10][11][12][13][14][15] Hz and TR is in [1][2][3][4][5]Hz. In addition to this, additive noise in sensors have a broad frequency range.…”

Section: Resultsmentioning

confidence: 99%

“…Several methods have been proposed for signal enhancement such as: Butterworth filter [13], Spectral Subtraction [14], Statistical model based methods (Hidden Markov Model) [15], sub-space based methods [16], etc. These methods have a high performance when dealing with uniform spectrum and additive noise.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events

Firoozabadi

Seguel

Soto

et al. 2017

Journal of Electrical Engineering

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Evaluation of seismic signals is one of the most important research topics on Volcanology. Volcanoes have daily activity; therefore, high speed evaluation of recorded signals is a challenge for improving the study of the natural phenomena occurring inside these natural formations. The aim of this paper is the evaluation (denoising, localization and classification) and analysis of Llaima volcano activities, one of the most actives volcanoes in South America. Different already proposed methods, such as, Butterworth, Spectral Subtraction (SS) and Wiener Filter (WF) are compared to the proposed Modified Spectral Subtraction (MSS) and Modified Wiener Filter (MWF) to find the best method for denoising the volcano signals. Then, event localization based on received signals of volcano is performed. In this step, Time Delay Estimation (TDE)-based method is used on data acquired from 3 mechanical sensors located in the volcano area. The proposed method is used to estimate the area for event location. The proposed denoising methods make the starting point for the event more evident to increase the localization accuracy for events where the starting point is difficult to find. In the last step, a method based on the novel DNN technique is proposed to classify the three main events occurring in the Llaima volcano (TR (Tremor), LP (Long Period) and VT (Volcano Tectonic)).

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“…Desain filter yang menjadi bahan perbandingan yaitu Butterworth, Chebyshev-I dan filter Elliptic. Hasil dari penelitiannya memamparkan bahwa desain filter dengan Butterworth memberikan hasil yang lebih baik jika dibandingkan dengan Chebyshev-I dan Elliptic berdasarkan hasil keluaran sinyal spektrumnya [4]. Perbandingan kinerja respon filter yang dilakukan oleh Prajoy juga dilakukan oleh Aung dkk pada tahun 2019.…”

Section: Pendahuluanunclassified

Perbandingan Kinerja Filter Butterworth Berdasarkan Spesifikasi Frekuensi untuk Pengolahan Sinyal Suara

Bustamin

Prayogi

2019

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Sinyal suara terkadang masih mengandung noise pada saat dilakukan proses pengolahan. Filter dilakukan untuk menyaring sinyal-sinyal suara yang tidak dibutuhkan ataupun dianggap mengganggu (noise). Penelitian ini bertujuan untuk melakukan preprocessing sinyal suara dengan membandingkan kinerja filter Infinite Impulse Respon (IIR) pada desain filter Butterworth berdasarkan frekuensi yang dilewatkan. Tujuan yang ingin dicapai dari tahap preprocessing yaitu mengolah suara agar dapat diambil karakteristik atau cirinya. Sinyal suara sampel yang akan difilter yaitu perekaman suara laki-laki pada frekuensi sampling 16000Hz. Pada penelitian ini filter Infinite Impulse Respon (IIR) digunakan dengan respon filter Butterworth. Validasi dilakukan dengan menghitung Signal Noise to Ratio (SNR) dari masing-masing jenis filter berdasarkan frekuensi yang dilewatkan. Hasil yang diperoleh berdasarkan nilai SNR tertinggi yaitu 29,9321 dB pada orde filter 6. Nilai dari SNR ini juga menunjukkan bahwa BPF lebih baik dibandingkan dengan LPF dan HPF.

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Design and Implementation of Butterworth, Chebyshev-I and Elliptic Filter for Speech Signal Analysis

Cited by 34 publications

References 9 publications

Wave Signatures in Total Electron Content Variations: Filtering Problems

Wave Signatures in Total Electron Content Variations: Filtering Problems

Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events

Perbandingan Kinerja Filter Butterworth Berdasarkan Spesifikasi Frekuensi untuk Pengolahan Sinyal Suara

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