Design and analysis of matrix Wiener synthesis filter for multirate filter bank

Patel, S.; Dhuli, Ravindra; Lall, Brejesh

doi:10.1016/j.sigpro.2014.03.021

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…On the basis of summarizing the previous studies and consulting a large amount of literatures, this article took five signal filtering methods as the research objects, including Basic Spectral Subtraction, Improved Spectral Subtraction based on multi-taper spectrum estimation, Wiener filtering, and Sparse Decomposition using thirty atoms and fifty atoms [ 22 , 23 , 24 , 25 , 26 ], to evaluate their filtering effect on broiler sound signal from multiple perspectives. In this chapter, the author briefly describes the principles of the five methods.…”

Section: Methodsmentioning

confidence: 99%

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Sun

Gao

Wang

et al. 2021

Animals

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Broiler sounds can provide feedback on their own body condition, to a certain extent. Aiming at the noise in the sound signals collected in broiler farms, research on evaluating the filtering methods for broiler sound signals from multiple perspectives is proposed, and the best performer can be obtained for broiler sound signal filtering. Multiple perspectives include the signal angle and the recognition angle, which are embodied in three indicators: signal-to-noise ratio (SNR), root mean square error (RMSE), and prediction accuracy. The signal filtering methods used in this study include Basic Spectral Subtraction, Improved Spectral Subtraction based on multi-taper spectrum estimation, Wiener filtering and Sparse Decomposition using both thirty atoms and fifty atoms. In analysis of the signal angle, Improved Spectral Subtraction based on multi-taper spectrum estimation achieved the highest average SNR of 5.5145 and achieved the smallest average RMSE of 0.0508. In analysis of the recognition angle, the kNN classifier and Random Forest classifier achieved the highest average prediction accuracy on the data set established from the sound signals filtered by Wiener filtering, which were 88.83% and 88.69%, respectively. These are significantly higher than those obtained by classifiers on data sets established from sound signals filtered by other methods. Further research shows that after removing the starting noise in the sound signal, Wiener filtering achieved the highest average SNR of 5.6108 and a new RMSE of 0.0551. Finally, in comprehensive analysis of both the signal angle and the recognition angle, this research determined that Wiener filtering is the best broiler sound signal filtering method. This research lays the foundation for follow-up research on extracting classification features from high-quality broiler sound signals to realize broiler health monitoring. At the same time, the research results can be popularized and applied to studies on the detection and processing of livestock and poultry sound signals, which has extremely important reference and practical value.

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Section: Methodsmentioning

confidence: 99%

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Sun

Gao

Wang

et al. 2021

Animals

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“…Before applying Wiener filtering on an NUFB, we convert it into an equivalent UFB with the help of blocking operation. This conversion is needed as Wiener filtering requires joint stationarity of the input and the desired signals [72]. Each subband of the NUFB with a decimation factor M i is blocked with a factor…”

Section: B Extension To Nufbmentioning

confidence: 99%

“…An equivalent representation of the process, as shown in Fig. 3b, can be derived using the noble identities [68,72]. Thus with blocking, the NUFB behaves like a UFB with decimation factor equal to M and with analysis filters given by…”

Section: B Extension To Nufbmentioning

confidence: 99%

Analysis of Signals via Non-Maximally Decimated Non-Uniform Filter Banks

Patel

Dhuli

Lall

2019

IEEE Trans. Circuits Syst. I

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This paper addresses the important problem of reconstructing a signal from multiple multirate observations. The observations are modeled as the output of an analysis bank, and time-domain analysis is carried out to design an optimal FIR synthesis bank. We pose this as a minimizing the mean-square problem and prove that at least one optimal solution is always possible. A parametric form for all optimal solutions is obtained for a non-maximally decimated filter bank. The necessary and sufficient conditions for an optimal solution, that results in perfect reconstruction (PR), are derived as time-domain pseudocirculant conditions. This represents a novel theoretical contribution in multirate filter bank theory. We explore PR in a more general setting. This results in the ability to design a synthesis bank with a particular delay in the reconstruction. Using this delay, one can achieve PR in cases where it might not have been possible otherwise. Further, we extend the design and analysis to nonuniform filter banks and carry out simulations to verify the derived results.

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“…In particular, an effective and an efficient design of this type of filter banks is challenging. To address this issue, the factorization approach [7,8,9,11,12,13,14,15,16,17,18,19,20,21] is proposed for designing this type of filter banks. However, as the filter bank is realized as the cascade of a set of lattices and these lattices are characterized by their rotational angles, the frequency responses of the filters are expressed as a set of very high order polynomials of the trigonometric functions of the filter coefficients.…”

mentioning

confidence: 99%

“…It is shown that the above paraunitary condition can be represented as a set of equations relating to the sum of the products of the frequency responses of the analysis filters. As these equality constraints are the quadratic equations of the filter coefficients, the problem formulation [22,23] is simpler compared to the existing problem formulations defined in the frequency domain [7,8,9,11,12,13,14,15,16,17,18,19,20,21]. Besides, this paper proposes to employ the norm relaxed sequential quadratic programming approach to find the solution of the optimization problem.…”

mentioning

confidence: 99%

Optimal maximally decimated <i>M</i>-channel mirrored paraunitary linear phase FIR filter bank design via norm relaxed sequential quadratic programming

Liu¹,

Ling²,

Dai³

et al. 2021

JIMO

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It is worth noting that the conventional maximally decimated Mchannel mirrored paraunitary linear phase finite impulse response condition is defined in the frequency domain. As the frequency domain is a continuous set, it is expressed as a matrix functional (a continuous function of the frequency) equation. On the other hand, this paper expresses the condition as a finite number of discrete (a set of functions of the sampled frequencies) equations. Besides, this paper proposes to sample the magnitude responses of the filters with the total number of the sampled frequencies being more than the filter lengths. Hence, the frequency selectivities of the filters can be controlled more effectively. This filter bank design problem is formulated as an optimization problem in such a way that the total mirrored paraunitary linear phase error is minimized subject to the specifications on the magnitude responses of the filters at these sampling frequencies. However, this optimization problem is highly nonconvex. To address this difficulty, a norm relaxed sequential quadratic programming approach is applied for finding its local optimal solution. By iterating the above procedures using different initial conditions, a near global optimal solution is obtained. Computer numerical simulation results show that our proposed design outperforms the existing designs.

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Design and analysis of matrix Wiener synthesis filter for multirate filter bank

Cited by 6 publications

References 12 publications

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Analysis of Signals via Non-Maximally Decimated Non-Uniform Filter Banks

Optimal maximally decimated <i>M</i>-channel mirrored paraunitary linear phase FIR filter bank design via norm relaxed sequential quadratic programming

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