Synthesis Filter Research Articles

Demonstration of a terahertz integrated planar network synthesis filter

At terahertz (THz) frequencies, there are few experimental works that demonstrate the viability of all-pole network synthesis filters to obtain desired frequency characteristics (i.e., Chebyshev, Butterworth, Bessel, etc.) using planar waveguides. This capability has been proven with non-planar waveguides, but has yet to be demonstrated using planar waveguides, which are desirable due to their integration capabilities similar to printed circuit board or monolithic microwave integrated circuit structures. In this paper, we use network synthesis methods from microwave engineering to fabricate three integrated planar low-pass filters for THz applications that have the same cut-off frequency (f c = 0.8 THz) but different orders (N = 3, 4, 5). We measure their response to a THz-bandwidth excitation pulse and find that the experimental results exhibit increasing roll-off rates with increasing filter order without incurring significant pulse distortion, which is aligned with theory.

Reconstruction of a signal from multirate observations: A recursive approach

In this paper, we develop a recursive formulation for the reconstruction of a signal from multirate observations. We model the observations as the outputs of a decimated analysis bank and convert the reconstruction problem into a matrix synthesis filter design problem. We use the mean-square criterion to minimize the reconstruction error and obtain a synthesis bank solution for a given filter length and delay in reconstruction. We then derive a recursive method with arbitrary step size for computing the same beginning from any smaller length solution. We also derive a recursive formulation for the mean-square error (MSE) with similar flexibility. This method is computationally efficient when compared to the direct computation of a solution. Additionally, it aids in studying the properties of the solutions as a function of filter length. Finally, we present experimental results that demonstrate the advantages of the recursive method developed.

Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT

This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions.

Channel estimation based on interference cancellation method for FBMC-PON

Based on the traditional orthogonal frequency division multiplexing passive optical network (OFDM-PON) system, filter bank multi-carrier passive optical network (FBMC-PON) eliminates the CP by introducing a synthesis filter, dramatically improving spectral efficiency when the number of symbols is sufficient in one transmission. Meanwhile, thanks to its excellent bit error rate (BER) performance, FBMC-PON becomes one of the significant alternatives to OFDM-PON. In previous practical cases, the interference approximation method (IAM) based channel estimation algorithm, which mainly enhances the power of training sequence, plays superior in FBMC-PON. However, the interference cancellation method (ICM) channel estimation method is a better choice due to the self-cancellation of imaginary interference, especially for short-distance optical fiber access channels. We analyze the four different training sequences and consider that in the short fiber channel of PON, it is more vital to suppress the imaginary interference rather than improve the training sequence’s power. The simulation results show that the BER performance of FBMC-PON based on our proposed ICM training sequence structure is one order of magnitude higher than that of OFDM-PON and half an order of magnitude higher than that of IAM in the case of 20 km fiber. Our proposed channel estimation algorithm only changes the structure of the training sequence and does not increase any additional computational complexity. As far as we know, ICM training sequence structure is verified in FBMC-PON by simulation for the first time. The results show that ICM is the optimal choice of the 4 kinds of training sequence structures. ICM training sequence structure has a very high application prospect in short reach FBMC-PON.

A New Approach to the Design and Implementation of a Family of Multiplier Free Orthogonal Wavelet Filter Banks

This paper presents a generalized novel design of a family of multiplier free orthogonal wavelet filter banks (FBs) with low complexity. In the proposed method, maximum number of zeros at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z=-1$ </tex-math></inline-formula> is ensured for a desired length of analysis and synthesis filters. A new class of technique is proposed to obtain dyadic filter coefficients based on double shifting orthogonality property with allowable deviation from original filter coefficients. This is achieved by slightly altering perfect reconstruction (PR) condition to obtain dyadic filter coefficient. A general closed form expression is formulated using double shifting orthogonality property to reduce the error between original filter coefficients and proposed dyadic filter coefficients. The designed wavelet FB improved time-frequency product, Sobolev regularity and frequency selectivity (low energy in error) over well known existing wavelet FBs. In addition, a fixed point VLSI architecture is proposed for the designed family of orthogonal wavelet FBs. The suggested architecture is designed and implemented on Kintex7 FPGA board. The proposed family of orthogonal wavelet FBs is multiplier less with significantly reduced adders, shifters and dynamic power dissipation. The performance of the proposed wavelet FB is validated on two different applications namely image compression and medical image retrieval. The proposed FBs are tested on well known datasets such as CLASSIC, EPFL, RAISE, and FiveK for image compression and on three different medical image databases such as NEMA, OASIS, and EXACT09 for image retrieval.

Differentiable Measures for Speech Spectral Modeling

Autoregressive models for the envelope of speech power spectral densities (PSDs) are refined by the self-supervised spectral learning machine (S3LM) provided with differentiable spectral objective functions, including the Itakura-Saito divergence (ISD), the Kullback-Leibler divergence (KLD), the reverse KLD (RKLD) and the log spectral distortion (LSD), which display more significant results. However, in order to assess the models more perceptually, a method is proposed based upon perturbations around perfect reconstruction analysis-synthesis configurations. In the cross-excitation analysis-synthesis assessment (CEASA) method, the residual signals generated by analysis filters of the spectral models are injected as excitation into the synthesis filters derived from the same and other models in order to be evaluated by the perceptual evaluation of speech quality (PESQ) and Itakura divergence (ID), which are averaged over a set of models obtained using the objective functions mentioned above. The results lead to a superior performance when the RKLD is used as the loss function for the estimation of the spectral models with the ISD ranking close behind. The focus of these divergences on the spectral peaks is argued and pointed as the most important factor for this behavior. Specifically, using the PESQ scores obtained with CEASA, the RKLD loss is found to improve the performance by 1.0%, 4.0% and 19.3% with respect to the open-loop analysis, the KLD and the LSD models, respectively, while the corresponding improvements for the ISD loss are 0.1%, 3.0% and 18.2%, and the RKLD models excel the ISD models by 1.0% on average. Even though the spectral measures alone are not able to unequivocally distinguish the better of the two, CEASA is shown to have enough sensitivity to distinguish their performances. In summary, the learning machine S3LM fits models for the short-term spectral envelope of speech and, for the evaluation of its performance under several differentiable loss functions, the CEASA assessment tool has been developed. In addition, CEASA may be used for other assessments connected with speech analysis and synthesis.

Research of application of DFT-modulated filter bank in systems with significant spectral component amplification

The purpose of this article is to investigate the application of DFT-modulated filter bank in systems with significant spectral component amplification like hearing aid. There is a description of analysis / synthesis method based on short-time Fourier transform (STFT), which is used in most systems of speech information processing. It is shown that DFT-modulated filter bank is a generalization of STFT-method. In analysis / synthesis system based on DFT-modulated filter bank, the input signal is divided into subbands, passing through the analysis filter bank then each subband is amplified and the last step is to reconstruct the signal with synthesis filter bank. However, in digital systems with significant spectral component amplification, the resulting signal is distorted after reconstruction because of amplification factor difference in each subband. The article provides expressions for the distortion and the aliasing functions, allowing to estimate the distortion value, which appears in analysis / synthesis system of DFT-modulated filter bank. Efficient algorithms for calculating the distortion and the aliasing functions are also offered. In future it is planning to develop a procedure for optimizing the DFT-modulated filter bank based on the proposed efficient algorithms for calculating distortion and spectral aliasing in the filter bank.

Artificial bandwidth extension using [formula omitted] sampled-data control theory

Artificial bandwidth extension of a speech signal is a way to improve speech quality and intelligibility in narrowband telephonic communication. Artificial bandwidth extension techniques extend the bandwidth of narrowband signals using only narrowband information available at the receiver end. This work proposes a new bandwidth extension technique based on the H∞ sampled-data control theory and deep neural network (DNN) regression approach for recovering the missing high-frequency components of the speech signal. The H∞ sampled-data control theory helps in designing of a synthesis filter by optimally utilizing the inter-sample information of a signal and a signal model. The obtained synthesis filter is further used to recover the high-frequency information of the signal. The non-stationary (time-varying) characteristic of speech signals mandates numerous synthesis filters for reconstructing the whole speech signal. Hence, a DNN model is used for estimating the synthesis filter information and a gain factor for specified narrowband information of an unseen signal. Objective analysis is done on the TIMIT and RSR15 datasets. Subjective analysis is done on the RSR15 dataset.

NANO-studio, the design environment of filter banks implemented in standard CMOS technology

The paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

Spectral Domain Spline Graph Filter Bank

In this paper, we present a structure for two-channel spline graph filter bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our proposed structure has many desirable properties; namely, perfect reconstruction, critical sampling in spectral domain, flexibility in choice of shape and cut-off frequency of the filters, and low complexity implementation of the synthesis section, thanks to our closed-form derivation of the synthesis filter and its sparse structure. These properties play a pivotal role in multi-scale transforms of graph signals. Additionally, this framework can use both normalized and non-normalized Laplacian of any undirected graph. We evaluate the performance of our proposed SGFBSS structure in nonlinear approximation and denoising applications through simulations. We also compare our method with the existing graph filter bank structures and show its superior performance.

High‐band feature extraction for artificial bandwidth extension using deep neural network andH∞optimisation

This work aims to enhance the quality of narrowband (0–4 kHz) voice signal in terms of frequency components, i.e. missing high-frequency components in a range of 4–8 kHz. The proposed artificial bandwidth extension framework uses the H ∞ optimisation. In this context, a signal model is used to get a better representation of wideband (0–8 kHz) information of a signal. The H ∞ optimisation is used to obtain the synthesis filter for a given signal model, which is used to synthesise the high-band (4–8 kHz) signal. The discrete Fourier transform addition is performed to add the narrowband signal and estimated high-band signal for removing the leaked information from the synthesis filter and non-ideal low pass filter. Gain adjustment is performed on the estimated high-band signal to make its energy equal to the true high-band signal. Non-stationary characteristics of speech signals generate an assorted variety in synthesis filters and corresponding gain. For this, a deep neural network (DNN) is used to estimate the synthesis filter and gain by using the given narrowband information. The authors analyse the performances of the DNN model on two data sets. Objective and subjective analyses are carried out on these data sets.

Особенности очистки отработавших газов судовых энергетических установок в пористых проницаемых каталитических материалах

&lt;b&gt;Object and purpose of research. &lt;/b&gt;This paper discusses exhaust gases of marine power plants to understand how performance and functionality of a catalytic neutralizer depend on the configuration and operational conditions of engine. &lt;br&gt;&lt;b&gt; Subject matter and methods.&lt;/b&gt;This paper discusses catalytic neutralization peculiarities of exhaust gases by porous permeable catalysts to mitigate the toxicity of emissions. &lt;br&gt;&lt;b&gt;Main results. &lt;/b&gt;It was found that the counter-pressure of a self-propagating high-temperature synthesis (SHS) filter depends on the number of its treatment stages and on diesel RPM. Comparative tests were performed for different configurations of catalytic neutralizers (one, two and three stages of exhaust gas treatment by porous permeable SHS filters) removing solid carbon particles from the emissions of a 3D6 (6Ch 15/18) diesels with different full-load curves. &lt;br&gt;&lt;b&gt; Conclusion. &lt;/b&gt;The test data outline design prospects and possible applications of catalytic neutralizers for treatment of marine diesel exhausts by porous SHS materials

Multiresolution Speech Enhancement Based on Proposed Circular Nested Microphone Array in Combination with Sub-Band Affine Projection Algorithm

Speech enhancement is one of the most important fields in audio and speech signal processing. The speech enhancement methods are divided into the single and multi-channel algorithms. The multi-channel methods increase the speech enhancement performance by providing more information with the use of more microphones. In addition, spatial aliasing is one of the destructive factors in speech enhancement strategies. In this article, we first propose a uniform circular nested microphone array (CNMA) for data recording. The microphone array increases the accuracy of the speech processing methods by increasing the information. Moreover, the proposed nested structure eliminates the spatial aliasing between microphone signals. The circular shape in the proposed nested microphone array implements the speech enhancement algorithm with the same probability for the speakers in all directions. In addition, the speech signal information is different in frequency bands, where the sub-band processing is proposed by the use of the analysis filter bank. The frequency resolution is increased in low frequency components by implementing the proposed filter bank. Then, the affine projection algorithm (APA) is implemented as an adaptive filter on sub-bands that were obtained by the proposed nested microphone array and analysis filter bank. This algorithm adaptively enhances the noisy speech signal. Next, the synthesis filters are implemented for reconstructing the enhanced speech signal. The proposed circular nested microphone array in combination with the sub-band affine projection algorithm (CNMA-SBAPA) is compared with the least mean square (LMS), recursive least square (RLS), traditional APA, distributed multichannel Wiener filter (DB-MWF), and multichannel nonnegative matrix factorization-minimum variance distortionless response (MNMF-MVDR) in terms of the segmental signal-to-noise ratio (SegSNR), perceptual evaluation of speech quality (PESQ), mean opinion score (MOS), short-time objective intelligibility (STOI), and speed of convergence on real and simulated data for white and colored noises. In all scenarios, the proposed method has high accuracy at different levels and noise types by the lower distortion in comparison with other works and, furthermore, the speed of convergence is higher than the compared researches.

Improvement of LMS adaptive noise canceller using uniform Poly-phase digital filter bank

&lt;span&gt;This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.&lt;/span&gt;

Design of Transmultiplexer Filter Banks Using Ramanujan Sums

Ramanujan sums are sequences introduced by the famous mathematician S. Ramanujan in 1918. Ramanujan sum $$c_{q}(n)$$ is the sequence in n with periodicity q. Many arithmetic functions such as Euler’s totient function and Riemann zeta function can be expressed using these sequences. These sums have many attractive properties which make them suitable for signal processing domain. Ramanujan sums are orthogonal and symmetric. Implementation of Ramanujan sum-based transforms is faster compared to discrete Fourier transform, since their computation involves only co-resonant frequencies. These transforms are particularly useful in identifying periodicities in signals. Ramanujan sums are integer valued. In this letter, it is proposed to use Ramanujan sums for the design of transmultiplexer filter banks. Here, the analysis and synthesis filter coefficients are integers. Computation time is reduced considerably by replacing floating point arithmetic with integer coefficients. Rounding errors in hardware implementation are avoided, and perfect reconstruction condition in the transmultiplexer system is ensured by using these integer coefficients.

On the Design of Two-Dimensional Quincunx Filterbanks with Directional Vanishing Moment Based on Eigenfilter Approach

This paper presents a two-dimensional (2-D) eigenfilter technique to design 2-D perfect reconstruction (PR) filterbanks with directional vanishing moment (DVM). In this paper, we first introduce the DVM constraint for 2-D directional filterbank design. The proposed DVM constraint is imposed in 2-D eigenfilter formulation to obtain maximally flat 2-D low-pass analysis filter. Then, we use this designed analysis filter in 2-D time-domain formulation of PR condition to obtain complementary low-pass synthesis filters. The PR and DVM constraints are imposed to obtain 2-D low-pass synthesis filter. It is shown that the proposed 2-D filters satisfy the PR criteria with prescribed DVM. The performance of 2-D PR filters is evaluated in image denoising application. The performance of designed filterbank is compared with well-known existing methods in terms of peak signal-to-noise ratio to validate the results.

Oversampled DFT-Modulated Biorthogonal Filter Banks: Perfect Reconstruction Designs and Multiplierless Approximations

We propose a novel methodology for designing oversampled discrete Fourier transform-modulated uniform filter banks. The analysis prototype is designed as a Nyquist filter, whereas the synthesis prototype is designed to guarantee perfect reconstruction (PR) considering oversampling. The resulting optimization problem fits into the disciplined convex programming framework, as long as some convex objective function is employed, as the minimization of either the stop-band energy or the maximum deviation from a desired response. The methodology also accounts for near-PR multiplierless approximations of the prototype analysis and synthesis filters, whose coefficients are obtained in the sum-of-power-of-two (SOPOT) space. The quantized coefficients are computed using successive approximation of vectors, which is able to yield filters with a reduced number of SOPOT coefficients in a computationally efficient manner. The proposed methodology is especially appealing for supporting actual hardware deployments, such as modern digital transparent processors to be used in next-generation satellite payloads.

MWA tied-array processing III: Microsecond time resolution via a polyphase synthesis filter

AbstractA new high time resolution observing mode for the Murchison Widefield Array (MWA) is described, enabling full polarimetric observations with up to$30.72\,$MHz of bandwidth and a time resolution of${\sim}$$0.8\,\upmu$s. This mode makes use of a polyphase synthesis filter to ‘undo’ the polyphase analysis filter stage of the standard MWA’s Voltage Capture System observing mode. Sources of potential error in the reconstruction of the high time resolution data are identified and quantified, with the$S/N$loss induced by the back-to-back system not exceeding$-0.65\,$dB for typical noise-dominated samples. The system is further verified by observing three pulsars with known structure on microsecond timescales.

Nonsubsampled Graph Filter Banks: Theory and Distributed Algorithms

In this paper, we consider nonsubsampled graph filter banks (NSGFBs) to process data on a sparse graph. The analysis filter banks of NSGFBs have small bandwidth, pass/block the normalized constant signal, and have stability on $\ell ^2$ . Given an analysis filter bank with small bandwidth, we introduce algebraic and optimization methods to construct well-localized synthesis filter banks such that the corresponding NSGFBs provide a perfect signal reconstruction in the noiseless setting. We also prove that the proposed NSGFBs can control the resonance effect in the presence of bounded noise and they can limit the influence of shot noise primarily to a small neighborhood near its location on the graph. We later introduce an iterative algorithm to implement the proposed NSGFBs in a distributed manner, and develop an NSGFB-based denoising technique which is demonstrated to have satisfactory performance on noise suppression.

Redefined Block-Lifting-Based Filter Banks With Efficient Reversible Nonexpansive Convolution

This paper redefines a block-lifting structure of $M$ -channel ( $M\in \mathbb {N}$ , $M\geq 2$ ) filter banks and proposes an efficient reversible nonexpansive convolution at the boundaries for lossy-to-lossless image coding. The previous studies left two problems. One is that the conventional lifting-based filter banks (FBs) are restricted to having equal analysis/synthesis filter lengths. We derive block-lifting-based FBs (BLFBs) with not only equal analysis/synthesis filter lengths but also the longer synthesis filter lengths than those of the analysis banks. The other problem is that the conventional lifting-based FBs without the linear-phase property, such as BLFBs, cannot implement the conventional smooth nonexpansive convolution at the boundaries because of the rounding error in each lifting. We solve the boundary problem by using an efficient reversible nonexpansive convolution derived from a nonexpansive convolution for nonlinear-phase FBs with paraunitariness. We show that the redefined BLFBs with the efficient reversible nonexpansive convolution perform well at lossy-to-lossless image coding.