Speech Enhancement Algorithm Based on a Hybrid Estimator

Mahmmod, Basheera M.; Abdulhussain, Sadiq H.; Naser, Marwah Abdulrazzaq; Alsabah, Muntadher; Mustafina, Jamila

doi:10.1088/1757-899x/1090/1/012102

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…Orthogonal polynomials (OPs) are considered efficient tools in several applications such as information hiding [30]- [32], face recognition [21], SBD [7], speech enhancement [33], [34], and handwritten numerical recognition [35]. The moments are the projection of signals on OPs [21], [36], [37].…”

Section: Preliminariesmentioning

confidence: 99%

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace. Video data is a rich source of information and considered the most demanding in terms of storage space. With the huge development of digital video production, video management becomes a challenging task. Video content analysis (VCA) aims to provide big data solutions by automating the video management. To this end, shot boundary detection (SBD) is considered an essential step in VCA. It aims to partition the video sequence into shots by detecting shot transitions. High computational cost in transition detection is considered a bottleneck for real-time applications. Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing. The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments. First, for each frame, the active area is selected such that only the informative content is considered. This leads to a reduction in the computational cost and disturbance factors. Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. For further elimination, two rounds of bisection comparisons are applied. As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified. The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively. Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%.

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Section: Preliminariesmentioning

confidence: 99%

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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“…This section presents the computation of the KP coefficients located at Part 2-3 in Figure 7. These values are computed using (21) in the ranges n = x 1 , x 1 + 1, N/2 − 2 and n + 2 ≤ x ≤ N − n + 1. However, the following condition should be met:…”

Section: Computation Of the Coefficients Located At Part 2-3mentioning

confidence: 99%

“…Continuous and discrete orthogonal polynomials are commonly used in many signal processing applications and feature characteristics. Continuous orthogonal polynomials are used in speech and image applications, for example, in pattern recognition, robot vision, face recognition, object classification, hiding information, data compression, template matching, and in edge detection for image data compression [ 20 , 21 , 22 , 23 ]. The performance of orthogonal polynomials is evaluated according to their ability to extract distinct features from signals in a fast and efficient way.…”

Section: Introductionmentioning

confidence: 99%

Reliable Recurrence Algorithm for High-Order Krawtchouk Polynomials

Al-Utaibi

Abdulhussain

Mahmmod

et al. 2021

Entropy

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Krawtchouk polynomials (KPs) and their moments are promising techniques for applications of information theory, coding theory, and signal processing. This is due to the special capabilities of KPs in feature extraction and classification processes. The main challenge in existing KPs recurrence algorithms is that of numerical errors, which occur during the computation of the coefficients in large polynomial sizes, particularly when the KP parameter (p) values deviate away from 0.5 to 0 and 1. To this end, this paper proposes a new recurrence relation in order to compute the coefficients of KPs in high orders. In particular, this paper discusses the development of a new algorithm and presents a new mathematical model for computing the initial value of the KP parameter. In addition, a new diagonal recurrence relation is introduced and used in the proposed algorithm. The diagonal recurrence algorithm was derived from the existing n direction and x direction recurrence algorithms. The diagonal and existing recurrence algorithms were subsequently exploited to compute the KP coefficients. First, the KP coefficients were computed for one partition after dividing the KP plane into four. To compute the KP coefficients in the other partitions, the symmetry relations were exploited. The performance evaluation of the proposed recurrence algorithm was determined through different comparisons which were carried out in state-of-the-art works in terms of reconstruction error, polynomial size, and computation cost. The obtained results indicate that the proposed algorithm is reliable and computes lesser coefficients when compared to the existing algorithms across wide ranges of parameter values of p and polynomial sizes N. The results also show that the improvement ratio of the computed coefficients ranges from 18.64% to 81.55% in comparison to the existing algorithms. Besides this, the proposed algorithm can generate polynomials of an order ∼8.5 times larger than those generated using state-of-the-art algorithms.

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“…Interpolation techniques can be used [49] for obtaining the channel covariance matrices at the base station. Advanced signal processing and machine learning methods can also be exploited to achieve this purpose [50][51][52].…”

Section: Physical Channel Model Under Arbitrary Array Configurationsmentioning

confidence: 99%

The Role of Correlation in the Performance of Massive MIMO Systems

Naser

Salman

Alsabah

2021

ASI

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Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly idealistic. Therefore, this study investigates the m-MIMO performance when the channels are correlated and the base station employs different antenna array topologies, namely the uniform linear array (ULA) and uniform rectangular array (URA). In addition, this study develops analyses of the mean square error (MSE) and the regularized zero-forcing (RZF) precoder under imperfect channel state information (CSI) and a realistic physical channel model. To this end, the MSE minimization and the spectral efficiency (SE) maximization are investigated. The results show that the SE is significantly degraded using the URA topology even when the RZF precoder is used. This is because the level of interference is significantly increased in the highly correlated channels even though the MSE is considerably minimized. This implies that using a URA topology with relatively high channel correlations would not be beneficial to the SE unless an interference management scheme is exploited.

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Speech Enhancement Algorithm Based on a Hybrid Estimator

Cited by 11 publications

References 24 publications

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

Reliable Recurrence Algorithm for High-Order Krawtchouk Polynomials

The Role of Correlation in the Performance of Massive MIMO Systems

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